allv
Variable x200
install.packages(“Hmisc”)
install.packages(“psych”)
install.packages(“rpart”)
install.packages(“Hmisc”)
library(rpart)
library(psych)
library(Hmisc)## Loading required package: lattice## Loading required package: survival##
## Attaching package: 'survival'## The following object is masked from 'package:rpart':
##
## solder## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha##
## Attaching package: 'Hmisc'## The following object is masked from 'package:psych':
##
## describe## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(data.table)
data = read.csv("E:/DS & NLP/dataset_00_with_header.csv", header = TRUE, na.strings = c("",".","NA"))
head(data)## x001 x002 x003 x004 x005 x006 x007 x008 x009 x010 x011 x012 x013 x014
## 1 1540332 NA NA NA 8 1 0 1 0 0 0 0 0 0
## 2 823066 4 3 3 4 0 2 2 0 0 0 0 0 0
## 3 1089795 NA NA NA 96 1 0 0 0 1 3 4 1 0
## 4 1147758 63 14 38 258 0 0 0 1 2 1 1 1 1
## 5 1229670 34 25 29 34 1 0 0 0 3 0 0 0 0
## 6 1292282 NA NA NA 126 0 0 0 0 0 1 0 0 2
## x015 x016 x017 x018 x019 x020 x021 x022 x023 x024 x025 x026 x027 x028
## 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0
## 2 2 2 0 2 2 0 0 0 0 2 0 1 0 0
## 3 9 0 9 2 0 9 0 2 0 0 0 0 1 0
## 4 7 2 5 5 2 4 1 3 1 3 0 0 1 0
## 5 3 2 1 2 1 3 2 2 1 0 0 0 1 0
## 6 3 0 3 2 0 1 0 1 0 2 0 1 0 0
## x029 x030 x031 x032 x033 x034 x035 x036 x037 x038 x039 x040 x041 x042
## 1 0 1 2 0 0 0 1 1 0 0 0 1 NA 0
## 2 1 0 14 7 8 10 11 8 1 2 4 5 NA 0
## 3 0 3 4 0 0 0 0 1 0 0 0 0 NA 0
## 4 0 3 1 0 0 1 1 1 0 0 1 1 1.5200 300
## 5 0 1 1 0 0 0 0 1 0 0 0 0 0.2111 900
## 6 2 0 1 0 0 1 1 1 0 0 1 1 NA 0
## x043 x044 x045 x046 x047 x048 x049 x050 x051 x052 x053 x054 x055 x056
## 1 0 300 300 0 0 0 0 0 0 0 0 0 0 0
## 2 0 NA NA 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1800 200 0 0 0 0 0 0 0 0 0 0 2
## 4 -156 4000 100 0 0 0 0 0 0 0 0 0 0 2
## 5 710 1000 300 1 0 0 0 0 0 1 0 0 0 0
## 6 0 200 200 0 0 0 0 0 0 0 0 0 0 1
## x057 x058 x059 x060 x061 x062 x063 x064 x065 x066 x067 x068 x069 x070
## 1 NA NA 0 0 0 0 0 0 0 0 0 0 0 0
## 2 NA NA 8 0 1 1 2 4 8 0 0 0 0 0
## 3 NA NA 3 0 0 0 0 0 3 0 0 0 0 0
## 4 1.52 1.5200 5 0 0 0 0 5 5 3 0 0 0 0
## 5 0.62 0.0066 7 0 1 1 3 7 7 1 0 0 0 0
## 6 NA NA 3 0 0 0 0 3 3 0 0 0 0 0
## x071 x072 x073 x074 x075 x076 x077 x078 x079 x080 x081 x082 x083 x084
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 7 0 22375 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 3287 1 0 0 0 0 0 1 0 0
## 4 3 3 5 3 684 1 0 0 0 0 0 0 0 0
## 5 1 1 3 1 2810 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 423 0 0 0 0 0 0 0 0 0
## x085 x086 x087 x088 x089 x090 x091 x092 x093 x094 x095 x096 x097 x098
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NA
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 NA
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 106
## 4 0 0 0 0 0 0 1 1 0 0 0 0 0 101
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 NA
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 NA
## x099 x100 x101 x102 x103 x104 x105 x106 x107 x108 x109 x110 x111 x112
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 1 0 0 0 0 0 2 2 0 0 0 5 2
## 5 1 1 0 0 0 0 0 2 2 0 0 0 2 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## x113 x114 x115 x116 x117 x118 x119 x120 x121 x122 x123 x124 x125 x126
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 4 5 5 5 2 4 5 5 5 0 0 0 0 0
## 5 1 2 2 2 0 1 2 2 2 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## x127 x128 x129 x130 x131 x132 x133 x134 x135 x136 x137 x138 x139 x140
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## x141 x142 x143 x144 x145 x146 x147 x148 x149 x150 x151 x152 x153 x154
## 1 0 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 4 0 0 0 0 0 0 1 2 0 1 1 1 1 0
## 5 0 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 NA 0 0 0 0 0 0
## x155 x156 x157 x158 x159 x160 x161 x162 x163 x164 x165 x166 x167 x168
## 1 NA 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 2 NA 0 0 0 0 0 0 1 0 0 0 0 0 0
## 3 NA 0 0 0 0 0 0 NA 0 0 0 0 0 5
## 4 NA 0 0 0 0 0 0 NA 0 0 0 0 0 0
## 5 NA 0 0 0 0 0 0 NA 0 0 0 0 0 1
## 6 NA 0 0 0 0 0 0 NA 0 0 0 0 0 1
## x169 x170 x171 x172 x173 x174 x175 x176 x177 x178 x179 x180 x181 x182
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 5 3 0 0 0 0 3 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 3 2
## 5 0 1 1 1 1 1 0 1 1 1 1 0 2 2
## 6 0 0 0 0 1 0 0 0 0 0 0 0 1 1
## x183 x184 x185 x186 x187 x188 x189 x190 x191 x192 x193 x194 x195 x196
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 1 9 3 6 7 7 7 5 8 9 9 9
## 5 0 0 0 2 0 1 2 2 2 0 1 2 2 2
## 6 0 0 0 10 0 0 0 3 9 0 0 0 3 9
## x197 x198 x199 x200 x201 x202 x203 x204 x205 x206 x207 x208 x209 x210
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 1 1 1 1 2 2 2 2 2 1 1 1 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## x211 x212 x213 x214 x215 x216 x217 x218 x219 x220 x221 x222 x223 x224
## 1 0 0 0 0 0 0 0 0 0 0 0 NA NA 0
## 2 0 0 0 0 0 0 0 0 0 0 0 NA NA 0
## 3 0 0 0 0 0 0 0 0 0 0 0 NA NA 0
## 4 1 1 1 1 1 1 0 0 0 0 0 1 1 0
## 5 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 NA NA 0
## x225 x226 x227 x228 x229 x230 x231 x232 x233 x234 x235 x236 x237 x238
## 1 0 0 0 0 0 0 0 1 0 0 300 0 NA NA
## 2 0 0 0 0 0 0 0 0 0 NA NA 0 NA NA
## 3 0 0 0 0 0 0 0 0 1613 1026 1800 0 NA NA
## 4 0 0 0 0 0 0 0 0 4796 4340 4000 -156 14 14
## 5 0 0 0 0 0 0 0 0 190 186 1000 710 34 25
## 6 0 0 0 0 0 0 0 0 319 319 200 0 NA NA
## x239 x240 x241 x242 x243 x244 x245 x246 x247 x248 x249 x250 x251 x252
## 1 NA 0 0 NA 0 0 1 1 1 0 0 0 0 0
## 2 NA 0 0 NA 0 0 0 0 0 0 0 0 0 0
## 3 NA 5 0 NA 0 0 1 1 1 0 0 0 0 0
## 4 14.0 2 1 1.52 456 1 1 1 1 0 1 1 1 0
## 5 29.5 3 1 0.62 186 1 1 1 1 0 0 0 0 0
## 6 NA 1 0 NA 0 0 1 1 1 0 0 0 0 0
## x253 x254 x255 x256 x257 x258 x259 x260 x261 x262 x263 x264 x265 x266
## 1 NA 0 NA NA NA 0 NA 0 0 0 0 0 NA NA
## 2 NA 0 NA NA NA 0 NA 0 0 0 1 4582 4 4
## 3 NA 0 NA NA NA 0 NA 0 0 0 0 0 NA NA
## 4 2 1 63 63 63 57762 0.8231 1 1 1 0 0 NA NA
## 5 NA 0 NA NA NA 0 NA 0 0 0 0 0 NA NA
## 6 NA 0 NA NA NA 0 NA 0 0 0 0 0 NA NA
## x267 x268 x269 x270 x271 x272 x273 x274 x275 x276 x277 x278 x279
## 1 NA NA 0 0 0 0.0000 0 0 NA 0 0 0 0
## 2 4 0.9256 1 1 1 0.9339 5206 0 NA 0 0 0 5206
## 3 NA NA 0 0 0 0.2281 1613 1613 0.4814 0 0 0 0
## 4 NA NA 0 0 0 0.8204 62558 4796 1.1153 1 2 2 58218
## 5 NA NA 0 0 0 0.1000 190 186 0.1430 1 1 1 190
## 6 NA NA 0 0 0 0.0100 319 319 0.0178 0 0 0 0
## x280 x281 x282 x283 x284 x285 x286 x287 x288 x289 x290 x291 x292
## 1 0 0 1 1 1 0 0 NA NA NA NA 0 0
## 2 0 5206 1 1 1 2 0 1 4 3 3.5 5206 5206
## 3 0 0 1 1 1 0 0 NA NA NA NA 0 0
## 4 58218 0 1 1 1 1 0 2 63 63 63.0 57762 57762
## 5 190 0 1 1 1 0 0 NA NA NA NA 0 0
## 6 0 0 1 1 1 0 0 2 NA NA NA 0 0
## x293 x294 x295 x296 x297 x298 x299 x300 x301 x302 x303 x304 y
## 1 NA 0 NA 0 NA 0 0 0 0 NA 0 NA 706
## 2 0.9339 0 NA 5206 0.9339 1 1 1 0 NA 0 NA 558
## 3 NA 0 NA 0 NA 0 0 0 0 NA 0 NA 577
## 4 0.8231 57762 0.8231 0 NA 1 1 1 0 NA 0 NA 526
## 5 NA 0 NA 0 NA 0 0 0 0 NA 0 NA 496
## 6 NA 0 NA 0 NA 0 0 0 0 NA 0 NA 528summary of the variable x200
summary(data$x200)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4873 0.0000 58.0000obeserve that values from 0 to 58, median is 0 there is no negative values & mean is 0.4873
Description of the variable x200
install.packages(“psych”)
library(psych)
describe(data$x200)## data$x200
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 42 0.42 0.4873 0.8881 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 3
##
## lowest : 0 1 2 3 4, highest: 40 42 48 49 58Here the kurtosis is high positive and skewness is the 7%
Total number of values excluding NA
NROW(data$x200) - NROW(data[which(is.na(data$x200)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x200)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 83395 7365 3299 1836 1214 743 564 355 298 190 155 107
## 12 13 14 15 16 17 18 19 20 21 22 23
## 92 65 65 52 30 42 20 17 22 9 5 12
## 24 25 26 27 28 29 30 31 32 33 34 36
## 9 4 3 4 4 2 3 1 3 1 7 1
## 37 40 42 48 49 58
## 1 1 1 1 1 1here we observe that 0 values clearly there more than 80%
finding the less than value 1 & 7
NROW(data[which(data$x200<=1),])## [1] 90760here we observed 90% less than 1
NROW(data[which(data$x200<=3),])## [1] 95895here we observed 95% are less than 5
Now visualize the statistics
Histogram
hist(data$x200,
main = "Histogram of x200",
xlab = "x200")
abline(v =mean(data$x200, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 has more frequnecy than others
Boxplot
boxplot(data$x200,
main = toupper("Boxplot of x200"),
ylab = "x200",
col = "red")
## here we obeserve that the box plot like single because meadian & Q are 0 ##Density Plot
d <- density(data$x200, na.rm = TRUE)
plot(d, main = "Kernel density of x200")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x200, na.rm = TRUE),col = "royalblue",
lwd = 2)
##obeserved: ## here by seeing the above graphs there is no missing values most of the values lies in b0 to 10 so it has descrete values.
summary of the variable x201
summary(data$x201)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.6328 0.0000 64.0000observe that values from 0 to 64, meadian is 0 & mean is 0.6328
Description of the variable x201
library(psych)
library(Hmisc)
describe(data$x201)## data$x201
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 47 0.476 0.6328 1.139 0 0
## .25 .50 .75 .90 .95
## 0 0 0 2 4
##
## lowest : 0 1 2 3 4, highest: 47 48 60 61 64Here the kurtosis is high positive and skewness is 7%
Total number of values excluding NA
NROW(data$x201) - NROW(data[which(is.na(data$x201)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x201)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 80606 8009 3815 2128 1544 887 747 430 395 263 244 172
## 12 13 14 15 16 17 18 19 20 21 22 23
## 153 98 81 62 60 51 29 20 41 28 19 17
## 24 25 26 27 28 29 30 31 32 34 36 37
## 15 11 10 8 9 7 6 1 5 3 6 2
## 39 40 42 43 45 46 47 48 60 61 64
## 3 1 1 1 1 5 2 1 1 1 1here we observe the 0 values clearly there more than 80%
finding the less than value 1 & 5
NROW(data[which(data$x201<=1),])## [1] 88615here we observed 88% less than 1
NROW(data[which(data$x201<=5),])## [1] 96989here we observed 96% are less than 5
Now visualize the statistics
Histogram
hist(data$x201,
main = "Histogram of x201",
xlab = "x201")
abline(v =mean(data$x201, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10 has more frequnecy than others because more values are in b/w 0 to 10.
Boxplot
boxplot(data$x201,
main = toupper("Boxplot of x201"),
ylab = "x201",
col = "red")
## observed the above graph box plot shows in the signal line because her median & Q1,Q3 are 0.. ##Density Plot
d <- density(data$x201, na.rm = TRUE)
plot(d, main = "Kernel density of x201")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x201, na.rm = TRUE),col = "royalblue",
lwd = 2)
## obeserve ##here 0 has more density there is no missing values most of the values lies in b0 to 5.so may be it has descrete values.
summary of the variable x202
summary(data$x202)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4992 0.0000 100.0000obeserve that values from 0 to 100,mean is 0.4992 & median is 0 beacuse most of the values is 0 so its taken median and q1 , q3 as zero.
Description of the variable x202
describe(data$x202)## data$x202
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 68 0.292 0.4992 0.9438 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 3
##
## lowest : 0 1 2 3 4, highest: 84 88 92 97 100Here the kurtosis is high positive and skewness is 13%.
Total number of values excluding NA
NROW(data$x202) - NROW(data[which(is.na(data$x202)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x202)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 89126 2559 2001 1630 1910 477 465 267 443 158 109 86
## 12 13 14 15 16 17 18 19 20 21 22 23
## 167 55 36 39 87 23 30 23 33 17 15 14
## 24 25 26 27 28 29 30 31 32 33 34 36
## 39 10 11 12 16 6 7 3 18 6 3 10
## 37 38 39 40 42 43 44 45 47 48 50 51
## 5 5 5 13 3 1 9 4 3 5 2 1
## 52 54 56 57 58 60 63 64 65 67 68 69
## 4 1 2 1 1 5 1 2 1 2 3 1
## 72 77 80 84 88 92 97 100
## 1 1 1 2 1 1 1 1here we observe the 0 values clearly there more than 89%
finding the less than value 1 & 7
NROW(data[which(data$x202<=1),])## [1] 91685here we observed 90% less than 1
NROW(data[which(data$x202<=7),])## [1] 98435here we observed 95% are less than 5
Now visualize the statistics
Histogram
hist(data$x202,
main = "Histogram of x202",
xlab = "x202")
abline(v =mean(data$x202, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 has more frequnecy than others and alo most the of the values less than 25.
Boxplot
boxplot(data$x202,
main = toupper("Boxplot of x202"),
ylab = "x202",
col = "red")
## observed the above graph box plot shows in the signal line because her median & Q1,Q3 are 0.
Density Plot
d <- density(data$x202, na.rm = TRUE)
plot(d, main = "Kernel density of x202")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x202, na.rm = TRUE),col = "royalblue",
lwd = 2)
##obeserve ## here 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 1.5,the skewness is a postive because the tail will have the large values.this the discrete values
summary of the variable x203
summary(data$x203)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.995 0.000 198.000observe that values from 0 to 198, mean is 0.4873 & median is 0.Bacaus the 80% of values are 0 so median & Q1
Description of the variable x203
describe(data$x203)## data$x203
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 112 0.375 0.995 1.861 0 0
## .25 .50 .75 .90 .95
## 0 0 0 2 6
##
## lowest : 0 1 2 3 4, highest: 157 160 170 184 198Here the kurtosis is high positive.
Total number of values excluding NA
NROW(data$x203) - NROW(data[which(is.na(data$x203)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x203)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 85516 2864 2268 1696 1326 892 843 569 605 475 795 186
## 12 13 14 15 16 17 18 19 20 21 22 23
## 327 125 153 111 151 57 125 36 132 54 37 28
## 24 25 26 27 28 29 30 31 32 33 34 35
## 63 24 25 27 35 13 35 10 36 9 8 11
## 36 37 38 39 40 41 42 43 44 45 46 47
## 31 5 11 14 22 7 13 2 7 14 3 3
## 48 49 50 51 52 53 54 55 56 57 58 60
## 28 8 11 3 5 1 13 4 4 3 6 6
## 61 62 63 64 65 66 67 68 69 70 71 72
## 1 1 6 5 4 6 2 2 2 5 1 6
## 73 74 76 77 78 79 80 82 84 87 88 89
## 2 2 2 2 1 1 3 1 4 1 3 2
## 90 91 94 96 97 98 99 101 102 103 104 105
## 2 1 2 1 1 2 3 3 2 1 1 4
## 109 112 113 116 117 122 126 132 146 150 151 157
## 3 2 1 1 1 1 1 3 1 1 1 1
## 160 170 184 198
## 1 1 1 1here we observe that 0 values are more than 80%
finding the values less than value 1 & 10
NROW(data[which(data$x203<=1),])## [1] 88380here we observed 88% less than 1
NROW(data[which(data$x203<=10),])## [1] 97849here we observed 97% are less than 10
Now visualize the statistics
Histogram
hist(data$x203,
main = "Histogram of x203",
xlab = "x203")
abline(v =mean(data$x203, na.rm = TRUE),col = "red",
lwd = 2)
## obeserve that 0 has more frequnecy than others and most of the values are less than 10
Boxplot
boxplot(data$x203,
main = toupper("Boxplot of x203"),
ylab = "x203",
col = "red")
## here thee boxplot is like single line because the median & Q1, Q3.
Density Plot
d <- density(data$x203, na.rm = TRUE)
plot(d, main = "Kernel density of x203")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x203, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observation ##here 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 0.424,the skewness is a postive because the tail will have the large values.hence these values are descrete values.
summary of the variable x204
summary(data$x204)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 2.036 0.000 280.000observe that values from 0 to 280, mean is 2.036 & median is 0 because most of the values are 0
Description of the variable x204
describe(data$x204)## data$x204
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 164 0.478 2.036 3.756 0 0
## .25 .50 .75 .90 .95
## 0 0 0 5 12
##
## lowest : 0 1 2 3 4, highest: 247 250 255 269 280Here the kurtosis is high positive.
Total number of values excluding NA
NROW(data$x204) - NROW(data[which(is.na(data$x204)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x204)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 80489 3346 2497 1905 1522 1041 1011 731 771 754 525 374
## 12 13 14 15 16 17 18 19 20 21 22 23
## 477 291 312 245 256 180 396 120 257 174 355 85
## 24 25 26 27 28 29 30 31 32 33 34 35
## 139 82 76 87 85 40 78 40 77 47 28 31
## 36 37 38 39 40 41 42 43 44 45 46 47
## 107 24 25 19 78 15 33 13 42 39 10 16
## 48 49 50 51 52 53 54 55 56 57 58 59
## 36 13 16 17 13 6 44 13 17 9 18 8
## 60 61 62 63 64 65 66 67 68 69 70 71
## 30 3 6 24 9 8 15 4 11 6 15 6
## 72 73 74 75 76 77 78 79 80 81 82 83
## 33 9 4 6 4 4 6 2 14 12 8 5
## 84 85 86 87 88 89 90 91 93 94 95 96
## 3 1 2 2 12 4 16 5 1 2 5 6
## 97 98 99 100 101 102 105 106 107 108 109 110
## 3 3 10 3 2 4 6 2 3 3 1 7
## 111 112 113 114 115 116 117 118 119 120 121 122
## 2 3 1 4 1 1 2 2 2 11 4 1
## 123 124 126 127 129 130 131 132 133 134 135 138
## 1 2 4 1 2 2 1 3 1 2 2 2
## 139 143 144 147 148 149 154 156 157 158 160 161
## 1 1 1 1 2 2 2 1 1 1 1 1
## 162 163 165 166 170 173 180 184 186 193 200 208
## 2 1 2 1 1 1 2 1 1 1 1 1
## 232 236 243 247 250 255 269 280
## 1 1 1 1 1 1 1 1here we observe that 0 values are more than 80%
finding the less than value 1 & 10
NROW(data[which(data$x204<=1),])## [1] 83835here we observed 83% less than 1
NROW(data[which(data$x204<=10),])## [1] 94592here we observed 94% are less than 5
Now visualize the statistics
Histogram
hist(data$x204,
main = "Histogram of x204",
xlab = "x204")
abline(v =mean(data$x204, na.rm = TRUE),col = "red",
lwd = 2)
## obeserve that 0 to 25 more frequnecy than others
Boxplot
boxplot(data$x204,
main = toupper("Boxplot of x204"),
ylab = "x204",
col = "red")
## we obsereved here the boxplot like single line because median & Q1 Q3 are 0.
Density Plot
d <- density(data$x204, na.rm = TRUE)
plot(d, main = "Kernel density of x204")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x204, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observe ## here 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 0.7618,the skewness is a postive because the tail will have the large values.so its has descrete values.
summary of the variable x205
summary(data$x205)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 3.052 0.000 434.000observe that values from 0 to 434, mean is 3.052 & median is 0 because most of the valus are 0
Description of the variable x205
describe(data$x205)## data$x205
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 202 0.548 3.052 5.563 0 0
## .25 .50 .75 .90 .95
## 0 0 0 8 18
##
## lowest : 0 1 2 3 4, highest: 300 305 328 330 434Here the kurtosis is high positive.
Total number of values excluding NA
NROW(data$x205) - NROW(data[which(is.na(data$x205)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x205)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 76716 3603 2671 1973 1656 1143 1091 862 873 936 671 516
## 12 13 14 15 16 17 18 19 20 21 22 23
## 604 373 396 326 362 222 435 177 306 179 226 146
## 24 25 26 27 28 29 30 31 32 33 34 35
## 248 128 144 137 152 105 219 106 127 100 127 71
## 36 37 38 39 40 41 42 43 44 45 46 47
## 142 43 40 67 91 34 55 25 45 49 22 30
## 48 49 50 51 52 53 54 55 56 57 58 59
## 63 27 34 30 33 16 52 25 35 15 23 17
## 60 61 62 63 64 65 66 67 68 69 70 71
## 56 15 31 36 24 16 27 8 20 11 25 10
## 72 73 74 75 76 77 78 79 80 81 82 83
## 40 13 9 14 9 11 16 2 22 14 6 11
## 84 85 86 87 88 89 90 91 92 93 94 95
## 17 4 7 6 13 3 14 13 7 11 7 9
## 96 97 98 99 100 101 102 103 104 105 106 107
## 9 5 8 15 7 1 6 3 5 9 4 4
## 108 109 110 111 112 113 114 115 116 117 118 119
## 7 4 6 2 4 5 1 3 3 3 2 2
## 120 121 122 123 124 125 126 127 128 129 130 131
## 11 5 2 1 2 3 5 3 2 5 3 5
## 132 133 134 135 136 138 139 140 141 142 143 144
## 4 2 4 6 3 3 1 5 1 1 3 6
## 145 146 147 148 149 152 153 154 156 157 158 159
## 3 1 3 4 1 7 1 7 3 1 2 2
## 160 161 163 165 167 169 170 171 172 173 174 176
## 2 1 1 3 1 3 2 2 3 1 1 2
## 179 180 184 185 190 191 194 196 197 198 200 202
## 1 3 1 1 2 1 1 1 1 1 2 1
## 203 204 210 211 214 217 218 229 234 235 236 237
## 1 1 3 1 1 1 1 1 2 1 2 1
## 240 243 262 276 288 300 305 328 330 434
## 1 1 1 1 1 1 1 1 1 1here we observe that 0 values more than 80%
finding the less than value 1 & 15
NROW(data[which(data$x205<=1),])## [1] 80319here we observed 80% less than 1
NROW(data[which(data$x205<=15),])## [1] 94410here we observed 94% are less than 5
Now visualize the statistics
Histogram
hist(data$x205,
main = "Histogram of x205",
xlab = "x205")
abline(v =mean(data$x205, na.rm = TRUE),col = "red",
lwd = 2)
## obeserve that 0 has more frequnecy than others
Boxplot
boxplot(data$x205,
main = toupper("Boxplot of x205"),
ylab = "x205",
col = "red")
## observe that box plot is like line because the median & Q1 Q3
Density Plot
d <- density(data$x205, na.rm = TRUE)
plot(d, main = "Kernel density of x205")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x205, na.rm = TRUE),col = "royalblue",
lwd = 2)
## obeserve that 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 1,the skewness is a postive because the tail will have the large values.these are descret values.
summary of the variable x206
summary(data$x206)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 4.069 1.000 632.000obeserve that values from 0 to 632, median is 0 & mean is 0.4873
Description of the variable x206
describe(data$x206)## data$x206
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 234 0.603 4.069 7.325 0 0
## .25 .50 .75 .90 .95
## 0 0 1 11 23
##
## lowest : 0 1 2 3 4, highest: 326 330 430 432 632Here the kurtosis is high positive.
Total number of values excluding NA
NROW(data$x206) - NROW(data[which(is.na(data$x206)),])## [1] 100000Here the value is 100000 because there is no missing values.
table(data$x206)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 73488 3771 2727 2080 1745 1248 1200 896 920 1059 794 575
## 12 13 14 15 16 17 18 19 20 21 22 23
## 650 468 485 390 423 302 484 263 443 228 276 185
## 24 25 26 27 28 29 30 31 32 33 34 35
## 288 191 197 182 216 130 204 112 155 110 112 106
## 36 37 38 39 40 41 42 43 44 45 46 47
## 162 82 100 92 124 76 137 58 80 89 80 40
## 48 49 50 51 52 53 54 55 56 57 58 59
## 85 26 48 46 48 23 71 39 57 21 29 15
## 60 61 62 63 64 65 66 67 68 69 70 71
## 63 19 31 40 39 23 36 19 20 19 46 23
## 72 73 74 75 76 77 78 79 80 81 82 83
## 42 21 24 18 12 23 18 8 32 17 13 10
## 84 85 86 87 88 89 90 91 92 93 94 95
## 33 9 10 10 15 8 18 15 15 12 11 7
## 96 97 98 99 100 101 102 103 104 105 106 107
## 15 11 12 24 10 6 7 7 7 10 4 7
## 108 109 110 111 112 113 114 115 116 117 118 119
## 13 4 6 3 9 4 10 5 9 6 1 5
## 120 121 122 123 124 125 126 127 128 129 130 131
## 14 7 2 4 4 5 8 2 4 5 7 7
## 132 133 134 135 136 137 138 139 140 141 142 143
## 6 8 3 3 4 3 2 2 7 2 4 4
## 144 145 146 147 148 150 151 152 153 154 156 157
## 4 2 5 3 2 5 4 4 1 5 1 2
## 158 159 160 161 162 163 165 166 167 168 169 170
## 3 1 6 2 2 2 3 2 1 5 4 3
## 171 172 173 174 175 176 178 179 180 182 184 185
## 2 3 5 2 2 2 3 2 2 3 3 2
## 186 187 188 190 191 196 198 199 200 203 204 206
## 2 1 2 2 4 2 2 3 2 1 1 1
## 207 210 211 212 214 217 219 221 223 225 226 227
## 1 1 2 1 2 1 1 1 1 2 1 2
## 230 233 234 235 236 237 240 241 243 244 246 248
## 2 1 1 1 4 1 1 1 1 1 1 1
## 249 252 256 260 262 264 267 274 275 284 304 305
## 1 1 1 2 1 1 1 1 1 1 2 1
## 325 326 330 430 432 632
## 1 1 1 1 1 1here we observe that 0 values are more than 70%.
finding the less than value 10 & 30
NROW(data[which(data$x206<=10),])## [1] 89928here we observed 89% less than 10
NROW(data[which(data$x206<=30),])## [1] 96508here we observed 96% are less than 30.
Now visualize the statistics
Histogram
hist(data$x206,
main = "Histogram of x206",
xlab = "x206")
abline(v =mean(data$x206, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 50 has more frequnecy than others
Boxplot
boxplot(data$x206,
main = toupper("Boxplot of x206"),
ylab = "x206",
col = "red")
## here found that the box plot is like a line because the median value is zero
Density Plot
d <- density(data$x206, na.rm = TRUE)
plot(d, main = "Kernel density of x206")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x206, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observe that 0 has more density there is no missing values most 0f the values lies in b0 to 50 and the bandwiidth is 0.06716,the skewness is a postive because the tail will have the large values
summary of the variable x207
summary(data$x207)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1168 0.0000 32.0000observe that values from 0 to 32, median is 0,Q1,Q3 is 0 & mean is 0.1168
Description of the variable x207
describe(data$x207)## data$x207
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 25 0.158 0.1168 0.2259 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 1
##
## lowest : 0 1 2 3 4, highest: 20 21 22 24 32Here the kurtosis is high positive.
Total number of values excluding NA
NROW(data$x207) - NROW(data[which(is.na(data$x207)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x207)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 94430 3305 1079 427 290 129 86 54 57 26 30 11
## 12 13 14 15 16 17 18 19 20 21 22 24
## 24 10 12 8 2 8 3 3 1 1 2 1
## 32
## 1here we observe that 0 values are more than 94%. there is skewness
finding the less than value 1 & 15
NROW(data[which(data$x207<=1),])## [1] 97735here we observed 97% less than 1
NROW(data[which(data$x207<=15),])## [1] 99978here we observed 99% are less than 15
Now visualize the statistics
Histogram
hist(data$x207,
main = "Histogram of x207",
xlab = "x207")
abline(v =mean(data$x207, na.rm = TRUE),col = "red",
lwd = 2)
##obeserve that 0 to 7 has more frequnecy than others
Boxplot
boxplot(data$x207,
main = toupper("Boxplot of x207"),
ylab = "x207",
col = "red")
## we observe that its showing like a line because the median value is 0 ##Density Plot
d <- density(data$x207, na.rm = TRUE)
plot(d, main = "Kernel density of x207")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x207, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation ## here the values are descrete values and the bandwiidth is 0.06,the skewness is a postive because the tail will have the large values
summary of the variable x208
summary(data$x208)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2112 0.0000 44.0000observe that values from 0 to 44, median is 0, Q1,Q3 are 0 & mean is 0.4873
Description of the variable x208
describe(data$x208)## data$x208
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 33 0.232 0.2112 0.4029 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 1
##
## lowest : 0 1 2 3 4, highest: 30 32 38 39 44Here the kurtosis is high positive and the skewness is 11 %
Total number of values excluding NA
NROW(data$x208) - NROW(data[which(is.na(data$x208)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x208)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 91559 4407 1773 681 562 234 189 95 129 70 68 37
## 12 13 14 15 16 17 18 19 20 21 22 23
## 44 27 26 18 16 14 13 3 7 5 5 4
## 24 25 26 27 30 32 38 39 44
## 4 1 1 2 2 1 1 1 1here we observe that 0 values are more than 90%
finding the less than value 1 & 6
NROW(data[which(data$x208<=1),])## [1] 95966here we observed 95% less than 1
NROW(data[which(data$x208<=6),])## [1] 99405here we observed 99% are less than 5
Now visualize the statistics
Histogram
hist(data$x208,
main = "Histogram of x208",
xlab = "x208")
abline(v =mean(data$x208, na.rm = TRUE),col = "red",
lwd = 2)
##observe that 0 to 7 has maximum values.
Boxplot
boxplot(data$x208,
main = toupper("Boxplot of x208"),
ylab = "x208",
col = "red")
## we can see the box plot shows like a line because its median & Q1, Q3 are 0
Density Plot
d <- density(data$x208, na.rm = TRUE)
plot(d, main = "Kernel density of x208")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x208, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation ## here 0 has more density there is no missing values most of the values lies in b0 to 10. so this have descrete values.
summary of the variable x209
summary(data$x209)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4068 0.0000 65.0000observe that values from 0 to 65, median is 0 & mean is 0.4068
Description of the variable x209
describe(data$x209)## data$x209
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 48 0.337 0.4068 0.7616 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 2
##
## lowest : 0 1 2 3 4, highest: 48 50 60 62 65Here the kurtosis is high positive and skewness is 10%
Total number of values excluding NA
NROW(data$x209) - NROW(data[which(is.na(data$x209)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x209)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 87191 5649 2779 1098 1023 441 427 180 273 153 145 76
## 12 13 14 15 16 17 18 19 20 21 22 23
## 109 49 60 40 53 35 47 18 24 19 15 9
## 24 25 26 27 28 29 30 31 32 33 34 36
## 15 3 8 7 5 2 12 4 3 2 4 3
## 37 38 40 42 44 45 47 48 50 60 62 65
## 2 1 5 2 2 1 1 1 1 1 1 1here we observe that 0 values are more than 80%
finding the less than value 1 & 10
NROW(data[which(data$x209<=1),])## [1] 92840here we observed 92% less than 1
NROW(data[which(data$x209<=10),])## [1] 99359here we observed 99% are less than 10
Now visualize the statistics
Histogram
hist(data$x209,
main = "Histogram of x209",
xlab = "x209")
abline(v =mean(data$x209, na.rm = TRUE),col = "red",
lwd = 2)
## obeserve that 0 has more frequnecy than others
Boxplot
boxplot(data$x209,
main = toupper("Boxplot of x209"),
ylab = "x209",
col = "red")
## here median & Q1 , Q3 are 0 so box plot showed like line
Density Plot
d <- density(data$x209, na.rm = TRUE)
plot(d, main = "Kernel density of x209")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x209, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation ## here we found 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 0.16,the skewness is a postive because the tail will have the large values.so this may be descrete values.
summary of the variable x210
summary(data$x210)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.588 0.000 100.000observe that values from 0 to 100, median is 0 & mean is 0.588
Description of the variable x210
describe(data$x210)## data$x210
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 60 0.407 0.588 1.087 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 3
##
## lowest : 0 1 2 3 4, highest: 64 65 77 81 100Here the kurtosis is high positive and the skewness is 10%.
Total number of values excluding NA
NROW(data$x210) - NROW(data[which(is.na(data$x210)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x210)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 83996 6427 3433 1456 1310 591 638 279 368 207 193 103
## 12 13 14 15 16 17 18 19 20 21 22 23
## 192 82 94 56 92 53 53 38 43 28 31 19
## 24 25 26 27 28 29 30 31 32 33 34 35
## 35 9 18 17 24 4 21 3 10 7 6 6
## 36 37 38 39 40 41 42 44 45 46 48 49
## 5 4 5 4 6 6 3 3 2 2 1 1
## 50 51 52 55 60 61 63 64 65 77 81 100
## 2 3 1 1 2 1 1 1 1 1 1 1here we observe that 0 has more than 80% value.
finding the less than value 2 & 20
NROW(data[which(data$x210<=2),])## [1] 93856here we observed 93% less than 2
NROW(data[which(data$x210<=10),])## [1] 98898here we observed 98% are less than 10
Now visualize the statistics
Histogram
hist(data$x210,
main = "Histogram of x210",
xlab = "x210")
abline(v =mean(data$x210, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 has more frequnecy than others ## Boxplot
boxplot(data$x210,
main = toupper("Boxplot of x210"),
ylab = "x210",
col = "red")
## here box plot shown like a lie because the median & Q1, Q3 are zero.
Density Plot
d <- density(data$x210, na.rm = TRUE)
plot(d, main = "Kernel density of x210")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x210, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation
here 0 has more density there is no missing values most of the values lies in b0 to 15 and the bandwiidth is 0.22,the value here may be descrete.
summary of the variable x211
summary(data$x211)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7641 0.0000 104.0000observe that values from 0 to 104, meadian is 0 & mean is 0.7641
Description of the variable x211
describe(data$x211)## data$x211
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 69 0.463 0.7641 1.397 0 0
## .25 .50 .75 .90 .95
## 0 0 0 2 4
##
## lowest : 0 1 2 3 4, highest: 81 82 86 97 104Here the kurtosis is high positive and skewness is 9%.
Total number of values excluding NA
NROW(data$x211) - NROW(data[which(is.na(data$x211)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x211)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 81244 7041 3905 1780 1587 712 803 356 494 275 274 141
## 12 13 14 15 16 17 18 19 20 21 22 23
## 233 109 126 86 109 62 86 50 59 41 46 28
## 24 25 26 27 28 29 30 31 32 33 34 35
## 48 21 22 22 25 11 31 13 23 10 9 11
## 36 37 38 39 40 41 42 43 44 45 46 47
## 8 5 7 8 8 9 5 1 8 8 4 2
## 48 49 50 51 52 55 56 59 60 61 63 64
## 1 1 3 7 3 2 1 2 1 2 1 1
## 65 68 75 78 81 82 86 97 104
## 1 1 1 1 1 1 1 1 1here we observe that more than 80% are 0 values
finding the less than value 2 & 12
NROW(data[which(data$x211<=2),])## [1] 92190here we observed 92% less than 2
NROW(data[which(data$x211<=12),])## [1] 98845here we observed 98% are less than 12
Now visualize the statistics
Histogram
hist(data$x211,
main = "Histogram of x211",
xlab = "x211")
abline(v =mean(data$x211, na.rm = TRUE),col = "red",
lwd = 2)
## obeserve that 0 has more frequnecy than others.
Boxplot
boxplot(data$x211,
main = toupper("Boxplot of x211"),
ylab = "x211",
col = "red")
## due to the median, quartile are 0 so we can see the box plot like a line.
Density Plot
d <- density(data$x211, na.rm = TRUE)
plot(d, main = "Kernel density of x211")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x211, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation: ## here 0 has more density there is no missing values most of the values lies in b0 to 15 and the bandwiidth is 0.2688.so the values are descrete type.
summary of the variable x212
summary(data$x212)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3623 0.0000 100.0000observe that values from 0 to 100, median is 0 & mean is 0.4873
Description of the variable x212
describe(data$x212)## data$x212
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 67 0.221 0.3623 0.6964 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 2
##
## lowest : 0 1 2 3 4, highest: 84 88 92 96 100Here the kurtosis is high positive and the skewness 16%.
Total number of values excluding NA
NROW(data$x212) - NROW(data[which(is.na(data$x212)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x212)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 92029 1934 1678 1053 1538 240 276 115 359 83 79 44
## 12 13 14 15 16 17 18 19 20 21 22 23
## 129 19 26 37 83 10 18 9 25 8 11 5
## 24 25 26 27 28 29 30 31 32 33 35 36
## 34 5 7 12 13 1 2 3 17 6 1 11
## 37 38 39 40 42 43 44 45 46 48 49 51
## 3 4 4 12 3 1 8 4 2 5 2 1
## 52 54 56 57 58 60 63 64 65 67 68 72
## 3 1 2 2 1 4 1 3 1 1 3 1
## 76 80 84 88 92 96 100
## 1 1 2 1 1 1 1here we observe that 92 % values are 0
finding the less than value 1 & 11
NROW(data[which(data$x212<=1),])## [1] 93963here we observed 92% less than 1
NROW(data[which(data$x212<=11),])## [1] 99428here we observed 99% are less than 11
Now visualize the statistics
Histogram
hist(data$x212,
main = "Histogram of x212",
xlab = "x212")
abline(v =mean(data$x212, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 has more frequnecy than others because they ar more than 90%.
Boxplot
boxplot(data$x212,
main = toupper("Boxplot of x212"),
ylab = "x212",
col = "red")
## due to the median,quartiles are 0 so it shows like a line.
Density Plot
d <- density(data$x212, na.rm = TRUE)
plot(d, main = "Kernel density of x212")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x212, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:
here 0 has more density there is no missing values most of the values lies in b0 to 12 and the bandwiidth is 0.2036. hence the value may be descrete values.
summary of the variable x213
summary(data$x213)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7813 0.0000 184.0000observe that values from 0 to 184, median is 0 & mean is 0.7813.
Description of the variable x213
describe(data$x213)## data$x213
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 109 0.292 0.7813 1.49 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 4
##
## lowest : 0 1 2 3 4, highest: 151 157 160 179 184Here the kurtosis is high positive and the skewness is 14%.
Total number of values excluding NA
NROW(data$x213) - NROW(data[which(is.na(data$x213)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x213)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 89124 2192 1947 1139 935 568 601 363 465 329 708 88
## 12 13 14 15 16 17 18 19 20 21 22 23
## 249 60 115 84 124 34 102 19 113 39 27 10
## 24 25 26 27 28 29 30 31 32 33 34 35
## 64 18 16 20 31 8 31 8 33 8 7 9
## 36 37 38 39 40 41 42 44 45 46 47 48
## 30 5 9 9 22 3 14 10 10 2 3 27
## 49 50 51 52 54 55 56 57 58 59 60 63
## 9 10 5 5 12 3 5 4 1 1 7 5
## 64 65 66 67 68 70 71 72 73 74 76 77
## 9 6 3 1 3 6 1 7 1 1 3 2
## 79 80 83 84 86 88 89 90 91 93 94 95
## 2 3 1 2 1 4 2 1 1 1 1 2
## 96 97 98 99 101 102 103 104 105 108 109 112
## 2 1 2 3 2 2 1 1 4 1 2 1
## 116 117 119 126 132 146 147 150 151 157 160 179
## 1 1 1 1 3 1 1 1 1 1 1 1
## 184
## 1here we observe that 89% of values are 0.
finding the less than value 1 & 10
NROW(data[which(data$x213<=1),])## [1] 91316here we observed 91% less than 1
NROW(data[which(data$x213<=10),])## [1] 98371here we observed 98% are less than 10
Now visualize the statistics
Histogram
hist(data$x213,
main = "Histogram of x213",
xlab = "x213")
abline(v =mean(data$x213, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 has more frequnecy than others because 0 more than 80%.
Boxplot
boxplot(data$x213,
main = toupper("Boxplot of x213"),
ylab = "x213",
col = "red")
## due to the median & quartiles are 0 so its shows like a line.
Density Plot
d <- density(data$x213, na.rm = TRUE)
plot(d, main = "Kernel density of x213")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x213, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation: ## here 0 has more density there is no missing values most of the values lies in b0 to 10 and the bandwiidth is 0.3.so this may be descrete value.
summary of the variable x214
summary(data$x214)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.695 0.000 280.000obeserve that values from 0 to 280, median is 0 & mean is 1.695
Description of the variable x214
describe(data$x214)## data$x214
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 166 0.387 1.695 3.196 0 0
## .25 .50 .75 .90 .95
## 0 0 0 3 9
##
## lowest : 0 1 2 3 4, highest: 243 250 255 268 280Here the kurtosis is high positive and the skewness is 11%
Total number of values excluding NA
NROW(data$x214) - NROW(data[which(is.na(data$x214)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x214)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 84965 2528 2087 1378 1106 726 753 516 585 573 431 207
## 12 13 14 15 16 17 18 19 20 21 22 23
## 371 183 230 176 227 120 345 89 215 154 318 35
## 24 25 26 27 28 29 30 31 32 33 34 35
## 136 57 48 65 77 34 71 20 64 47 25 22
## 36 37 38 39 40 41 42 43 44 45 46 47
## 108 19 25 21 72 8 26 14 38 39 11 14
## 48 49 50 51 52 53 54 55 56 57 58 59
## 31 16 11 8 14 5 42 13 20 11 11 4
## 60 61 62 63 64 65 66 67 68 69 70 71
## 30 5 4 22 15 8 18 6 9 1 12 8
## 72 73 74 75 76 77 78 79 80 81 82 83
## 35 4 1 6 4 7 6 2 13 15 5 6
## 84 85 86 87 88 89 90 91 92 93 94 95
## 3 2 1 1 11 3 16 4 2 2 2 4
## 96 97 98 99 100 101 102 103 104 105 106 107
## 7 2 2 9 2 3 3 1 2 5 1 2
## 108 109 110 111 112 113 114 115 117 118 119 120
## 3 2 6 2 4 1 3 1 2 2 3 11
## 121 122 123 126 127 129 130 131 132 133 134 135
## 3 1 1 4 1 2 2 1 3 1 2 2
## 137 138 139 143 144 146 147 148 149 154 156 157
## 1 1 1 1 1 1 2 1 1 2 1 1
## 158 160 161 162 163 165 166 170 172 180 184 193
## 2 2 1 1 1 1 1 2 1 2 1 1
## 199 203 207 223 231 243 250 255 268 280
## 1 1 1 1 1 1 1 1 1 1here we observe that 84% are 0 values.
finding the less than value 5 & 25
NROW(data[which(data$x214<=5),])## [1] 92790here we observed 92% less than 5
NROW(data[which(data$x214<=25),])## [1] 98511Now visualize the statistics
##Histogram
hist(data$x214,
main = "Histogram of x214",
xlab = "x214")
abline(v =mean(data$x214, na.rm = TRUE),col = "red",
lwd = 2)
##obeserve that 0 has more frequnecy than othersBoxplot
boxplot(data$x214,
main = toupper("Boxplot of x214"),
ylab = "x214",
col = "red")
## Due to the median & quartiles are 0 , so its shows like a single line
Density Plot
d <- density(data$x214, na.rm = TRUE)
plot(d, main = "Kernel density of x214")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x214, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation: ## here 0 has more density there is no missing values most of the values lies in b0 to 15 and the bandwidth is 0.7,so the values may be descrete.
summary of the variable x215
summary(data$x215)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 2.565 0.000 434.000observe that values from 0 to 434, median is 0 & mean is 2.565
Description of the variable x215
describe(data$x215)## data$x215
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 196 0.453 2.565 4.779 0 0
## .25 .50 .75 .90 .95
## 0 0 0 5 15
##
## lowest : 0 1 2 3 4, highest: 297 300 305 309 434Here the kurtosis is high positive and the skewness is 9%
Total number of values excluding NA
NROW(data$x215) - NROW(data[which(is.na(data$x215)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x215)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 81776 2715 2206 1462 1262 795 799 623 709 735 557 327
## 12 13 14 15 16 17 18 19 20 21 22 23
## 475 235 307 262 310 172 409 130 228 124 172 93
## 24 25 26 27 28 29 30 31 32 33 34 35
## 209 100 125 125 144 72 196 77 117 72 121 46
## 36 37 38 39 40 41 42 43 44 45 46 47
## 157 27 44 51 79 19 46 20 32 58 28 21
## 48 49 50 51 52 53 54 55 56 57 58 59
## 60 25 23 18 30 13 56 28 31 13 25 11
## 60 61 62 63 64 65 66 67 68 69 70 71
## 58 20 21 29 27 22 25 3 16 8 23 9
## 72 73 74 75 76 77 78 79 80 81 82 83
## 44 12 5 10 12 9 10 4 20 18 5 8
## 84 85 86 87 88 89 90 91 92 93 94 95
## 14 4 6 3 11 4 19 10 7 8 5 7
## 96 97 98 99 100 101 102 103 104 105 106 107
## 14 1 10 14 8 1 8 5 6 4 3 2
## 108 109 110 112 113 115 116 117 118 119 120 121
## 4 2 8 9 1 6 4 1 2 3 6 3
## 122 123 124 125 126 127 128 129 130 131 132 133
## 2 1 2 4 8 3 3 3 3 1 5 1
## 134 135 136 137 138 139 140 141 142 143 144 145
## 3 6 2 3 2 2 5 1 2 4 6 1
## 146 147 148 149 150 151 152 153 154 156 157 158
## 2 1 2 2 2 2 2 2 5 1 1 2
## 159 160 161 162 164 165 167 168 169 170 171 172
## 1 2 2 1 1 4 1 1 2 2 1 3
## 173 177 180 183 184 191 194 196 197 199 201 203
## 1 1 3 2 1 2 1 1 1 1 2 1
## 207 210 211 212 228 234 235 240 243 244 268 297
## 2 3 1 1 2 2 2 1 1 1 1 1
## 300 305 309 434
## 1 1 1 1here we observe that 81% are 0 values.
finding the less than value 5 & 18
NROW(data[which(data$x215<=5),])## [1] 90216here we observed 90% less than 5
NROW(data[which(data$x215<=18),])## [1] 96136here we observed 96% are less than 18
Now visualize the statistics
Histogram
hist(data$x215,
main = "Histogram of x215",
xlab = "x215")
abline(v =mean(data$x215, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 20 has more frequnecy than others 81 % of values are 0.
Boxplot
boxplot(data$x215,
main = toupper("Boxplot of x215"),
ylab = "x215",
col = "red")
## Due to the median & Quartiles are 0 we see the box plot like a line.
Density Plot
d <- density(data$x215, na.rm = TRUE)
plot(d, main = "Kernel density of x215")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x215, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values are lies in b0 to 20 and the bandwidth is 0.99,May be the data is a descrete value.
summary of the variable x216
summary(data$x216)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 3.436 0.000 632.000observe that values from 0 to 632, median is 0 & mean is 3.436
Description of the variable x216
describe(data$x216)## data$x216
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 225 0.509 3.436 6.329 0 0
## .25 .50 .75 .90 .95
## 0 0 0 8 20
##
## lowest : 0 1 2 3 4, highest: 305 324 390 391 632Here the kurtosis is high positive and the skewness is 9%.
Total number of values excluding NA
NROW(data$x216) - NROW(data[which(is.na(data$x216)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x216)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 78903 2903 2284 1556 1329 892 875 682 738 864 629 399
## 12 13 14 15 16 17 18 19 20 21 22 23
## 551 292 413 285 365 239 492 181 364 166 203 134
## 24 25 26 27 28 29 30 31 32 33 34 35
## 267 144 167 173 188 92 191 78 132 87 93 83
## 36 37 38 39 40 41 42 43 44 45 46 47
## 171 58 81 73 102 58 120 37 70 85 69 33
## 48 49 50 51 52 53 54 55 56 57 58 59
## 84 28 30 27 42 21 76 34 60 8 24 18
## 60 61 62 63 64 65 66 67 68 69 70 71
## 72 30 27 39 34 34 25 8 20 12 39 17
## 72 73 74 75 76 77 78 79 80 81 82 83
## 49 12 14 11 19 14 19 6 21 16 14 10
## 84 85 86 87 88 89 90 91 92 93 94 95
## 27 10 11 7 17 6 17 9 18 9 9 9
## 96 97 98 99 100 101 102 103 104 105 106 107
## 20 11 8 10 10 3 9 6 10 10 6 9
## 108 109 110 111 112 113 114 115 116 117 118 119
## 15 4 10 3 7 2 7 4 8 6 5 3
## 120 121 122 123 124 125 126 127 128 129 130 131
## 11 7 2 2 2 7 12 3 5 2 4 5
## 132 133 134 135 136 137 138 139 140 142 144 145
## 7 6 3 6 2 4 1 1 7 1 4 4
## 146 147 148 149 150 151 152 153 154 155 156 157
## 2 3 2 1 3 4 3 3 3 1 2 2
## 158 159 160 161 162 165 166 167 168 169 170 171
## 3 2 4 4 2 5 3 3 2 3 3 1
## 172 173 174 175 176 178 179 180 181 184 186 190
## 3 3 1 1 4 2 2 4 1 3 1 2
## 191 192 196 198 199 200 201 203 204 206 207 210
## 3 1 2 3 3 1 2 1 1 1 1 1
## 213 214 215 219 220 221 225 227 228 230 232 234
## 1 1 1 2 1 1 2 3 1 1 1 1
## 235 237 243 244 246 249 256 260 261 263 266 267
## 3 1 2 2 1 1 1 1 1 1 1 1
## 274 287 297 304 305 324 390 391 632
## 1 1 1 1 1 1 1 1 1here we observe that 78% are 0 values.
finding the less than value of 25 & 50
NROW(data[which(data$x216<=25),])## [1] 96150here we observed 96% less than 25
NROW(data[which(data$x216<=50),])## [1] 98533here we observed 98% are less than 50
Now visualize the statistics
Histogram
hist(data$x216,
main = "Histogram of x216",
xlab = "x216")
abline(v =mean(data$x216, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 0 has more frequnecy than others.
Boxplot
boxplot(data$x216,
main = toupper("Boxplot of x216"),
ylab = "x216",
col = "red")
## Due to the median & Quartiles are 0 the box plot is like a line.
Density Plot
d <- density(data$x216, na.rm = TRUE)
plot(d, main = "Kernel density of x216")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x216, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:
Here 0 has more density there is no missing values most of the values lies in b0 to 50 and the bandwiidth is 1.2,the values are descret type.
summary of the variable x217
summary(data$x217)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2455 0.0000 100.0000observe that values from 0 to 100,median is 0 & mean is 0.2455
Description of the variable x217
describe(data$x217)## data$x217
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 59 0.157 0.2455 0.4781 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 1
##
## lowest : 0 1 2 3 4, highest: 72 76 80 92 100Here the kurtosis is high positive and the skewness is 20%
Total number of values excluding NA
NROW(data$x217) - NROW(data[which(is.na(data$x217)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x217)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 94449 1652 1008 601 1197 84 191 54 272 44 44 18
## 12 13 14 15 16 17 18 19 20 21 22 23
## 95 13 11 13 61 5 16 1 16 6 8 4
## 24 26 27 28 30 31 32 33 34 35 36 37
## 21 5 3 11 6 2 12 4 4 1 9 3
## 38 39 40 42 43 44 45 48 52 53 55 56
## 3 3 7 2 1 8 3 3 2 1 1 2
## 57 60 63 64 67 68 72 76 80 92 100
## 1 3 2 2 1 2 3 1 3 1 1here we observe that 94% are 0 values.
finding the less than value 1 & 7
NROW(data[which(data$x217<=1),])## [1] 96101here we observed 96% less than 1
NROW(data[which(data$x217<=7),])## [1] 99236here we observed 99% are less than 7
Now visualize the statistics
Histogram
hist(data$x217,
main = "Histogram of x217",
xlab = "x217")
abline(v =mean(data$x217, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 7 has more frequnecy than others
Boxplot
boxplot(data$x217,
main = toupper("Boxplot of x217"),
ylab = "x217",
col = "red")
## Due to the median & quartiles are 0 its dosplay like a line.
Density Plot
d <- density(data$x217, na.rm = TRUE)
plot(d, main = "Kernel density of x217")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x217, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the bandwidth is 0.17, the values ar descrete.
summary of the variable x218
summary(data$x218)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5701 0.0000 177.0000observe that values from 0 to 177, median is 0 & mean is 0.5701
Description of the variable x218
describe(data$x218)## data$x218
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 97 0.217 0.5701 1.103 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 2
##
## lowest : 0 1 2 3 4, highest: 140 146 151 161 177Here the kurtosis is high positive and the skewness is 16%.
Total number of values excluding NA
NROW(data$x218) - NROW(data[which(is.na(data$x218)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x218)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 92181 1838 1279 660 617 353 444 253 355 242 596 54
## 12 13 14 15 16 17 18 19 20 21 22 23
## 178 33 85 45 114 19 65 9 98 27 14 7
## 24 25 26 27 28 29 30 31 32 33 34 35
## 51 11 12 15 28 3 29 3 26 7 10 11
## 36 37 38 39 40 41 42 43 44 45 46 47
## 16 4 7 4 18 1 17 6 1 6 4 2
## 48 49 50 51 52 53 54 55 56 57 58 60
## 16 5 4 1 9 4 6 5 6 1 1 6
## 63 64 65 66 67 70 71 72 73 74 75 76
## 4 5 1 4 1 6 1 3 2 1 1 2
## 77 78 79 80 84 85 86 88 90 91 94 96
## 3 2 2 4 1 1 1 5 6 1 2 1
## 98 101 103 104 108 118 125 139 140 146 151 161
## 1 1 1 1 5 2 1 1 1 1 1 1
## 177
## 1here we observe that 92% of values are 0.
finding the less than value 1 & 7
NROW(data[which(data$x218<=1),])## [1] 94019here we observed 94% less than 1
NROW(data[which(data$x218<=7),])## [1] 97625here we observed 95% are less than 7
Now visualize the statistics
Histogram
hist(data$x218,
main = "Histogram of x218",
xlab = "x218")
abline(v =mean(data$x218, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 15 has more frequnecy.
Boxplot
boxplot(data$x218,
main = toupper("Boxplot of x218"),
ylab = "x218",
col = "red")
## Due to the median & Quartilers its shows like a line .
Density Plot
d <- density(data$x218, na.rm = TRUE)
plot(d, main = "Kernel density of x218")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x218, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation ## Here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the values are descrete data types.
summary of the variable x219
summary(data$x219)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.289 0.000 240.000observe that values from 0 to 240, meadian is 0 & mean is 1.289
Description of the variable x219
describe(data$x219)## data$x219
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 142 0.303 1.289 2.464 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 7
##
## lowest : 0 1 2 3 4, highest: 200 224 225 228 240Here the kurtosis is high positive and the skewness is 11%.
Total number of values excluding NA
NROW(data$x219) - NROW(data[which(is.na(data$x219)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x219)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 88655 2122 1518 830 766 461 588 366 654 263 297 158
## 12 13 14 15 16 17 18 19 20 21 22 23
## 268 126 179 122 346 93 174 55 202 134 292 28
## 24 25 26 27 28 29 30 31 32 33 34 35
## 123 28 46 43 67 18 57 12 93 24 15 20
## 36 37 38 39 40 41 42 43 44 45 46 47
## 49 14 13 10 63 7 38 14 31 21 14 4
## 48 49 50 51 52 53 54 55 56 57 58 59
## 50 15 17 5 11 4 15 12 31 2 4 2
## 60 61 62 63 64 65 66 67 68 69 70 71
## 21 3 5 13 25 8 6 5 2 3 6 5
## 72 74 75 76 77 78 79 80 81 82 83 84
## 30 2 4 6 6 7 1 18 2 1 1 3
## 85 86 87 88 90 91 92 94 95 96 97 98
## 3 4 5 9 6 2 1 1 3 5 1 2
## 99 100 101 102 103 104 105 106 107 108 110 112
## 5 1 2 1 1 2 8 2 2 6 1 4
## 113 114 115 117 118 120 121 122 124 126 127 130
## 1 1 2 3 3 4 3 2 1 1 1 2
## 133 134 137 138 139 144 148 149 150 154 156 160
## 1 1 1 1 1 2 1 2 1 2 1 1
## 161 162 164 177 194 200 224 225 228 240
## 2 1 1 2 1 1 1 1 1 1here we observe that 88% of values are 0.
finding the less than value 1 & 10
NROW(data[which(data$x219<=1),])## [1] 90777here we observed 90% less than 1
NROW(data[which(data$x219<=10),])## [1] 96520here we observed 96% are less than 10
Now visualize the statistics
##Histogram
hist(data$x219,
main = "Histogram of x219",
xlab = "x219")
abline(v =mean(data$x219, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 20 has more frequnecy.
Boxplot
boxplot(data$x219,
main = toupper("Boxplot of x219"),
ylab = "x219",
col = "red")
## Due to the median & Quartiles are 0 so its shows like line.
Density Plot
d <- density(data$x219, na.rm = TRUE)
plot(d, main = "Kernel density of x219")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x219, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the skewness is a postive because the tail will have the large values. Here the values are desceret values.
summary of the variable x220
summary(data$x220)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.977 0.000 392.000observed that values from 0 to 392, meadian is 0 & mean is 1.977
Description of the variable x220
describe(data$x220)## data$x220
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 174 0.367 1.977 3.736 0 0
## .25 .50 .75 .90 .95
## 0 0 0 3 11
##
## lowest : 0 1 2 3 4, highest: 234 240 248 261 392Here the kurtosis is high positive and the skewness is 10%.
Total number of values excluding NA
NROW(data$x220) - NROW(data[which(is.na(data$x220)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x220)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 85841 2214 1686 949 899 488 650 450 887 405 398 223
## 12 13 14 15 16 17 18 19 20 21 22 23
## 378 168 280 192 466 110 248 79 167 119 114 79
## 24 25 26 27 28 29 30 31 32 33 34 35
## 176 89 103 91 114 44 169 65 158 78 101 33
## 36 37 38 39 40 41 42 43 44 45 46 47
## 76 20 30 29 76 22 42 23 40 32 19 18
## 48 49 50 51 52 53 54 55 56 57 58 59
## 78 18 32 15 22 11 37 19 40 9 11 9
## 60 61 62 63 64 65 66 67 68 69 70 71
## 34 6 14 15 40 10 14 7 22 4 15 6
## 72 73 74 75 76 77 78 79 80 81 82 83
## 35 7 6 11 3 9 14 9 13 8 2 4
## 84 85 86 87 88 89 90 91 92 93 94 95
## 8 2 1 4 16 3 14 5 4 5 7 3
## 96 97 98 99 100 101 102 103 104 105 106 107
## 9 5 4 5 1 2 1 3 4 4 4 2
## 108 109 110 111 112 113 114 116 117 118 119 120
## 4 3 4 4 10 3 1 3 5 5 1 4
## 121 122 123 125 126 127 128 130 131 132 133 134
## 3 1 1 1 4 2 3 6 1 3 5 2
## 135 136 137 138 139 140 141 143 144 146 147 149
## 1 3 4 2 3 1 1 3 1 1 1 2
## 150 152 153 154 159 160 161 162 164 168 171 175
## 2 1 1 1 1 1 1 1 1 1 1 1
## 177 178 183 184 191 192 194 198 200 216 220 224
## 2 2 1 1 1 1 2 1 2 1 1 1
## 228 234 240 248 261 392
## 1 1 1 1 1 1here we observe that 80% of values are 0.
finding the less than value 1 & 7
NROW(data[which(data$x220<=1),])## [1] 88055here we observed 88% less than 1
NROW(data[which(data$x220<=7),])## [1] 93177here we observed 93% are less than 7
Now visualize the statistics
Histogram
hist(data$x220,
main = "Histogram of x220",
xlab = "x220")
abline(v =mean(data$x220, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 100 only has frequnecy than others.
Boxplot
boxplot(data$x220,
main = toupper("Boxplot of x220"),
ylab = "x220",
col = "red")
## Due to median & Quartiles are 0 so its shown like a line.
Density Plot
d <- density(data$x220, na.rm = TRUE)
plot(d, main = "Kernel density of x220")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x220, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## here 0 has more density there is no missing values most of the values are lies in b0 to 100 and the skewness is a postive because the tail will have the large values, so the it has descret values.
summary of the variable x221
summary(data$x221)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 2.672 0.000 557.000observe that values from 0 to 557, median is 0 & mean is 2.672
Description of the variable x221
describe(data$x221)## data$x221
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 204 0.423 2.672 4.993 0 0
## .25 .50 .75 .90 .95
## 0 0 0 6 16
##
## lowest : 0 1 2 3 4, highest: 269 290 305 308 557Here the kurtosis is high positive and the skewness is 9%.
Total number of values excluding NA
NROW(data$x221) - NROW(data[which(is.na(data$x221)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x221)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 83242 2353 1750 1062 965 569 733 518 969 511 498 298
## 12 13 14 15 16 17 18 19 20 21 22 23
## 433 222 360 230 583 159 343 129 226 148 168 113
## 24 25 26 27 28 29 30 31 32 33 34 35
## 272 103 129 151 138 64 127 55 179 64 78 57
## 36 37 38 39 40 41 42 43 44 45 46 47
## 101 42 63 48 89 49 127 35 53 57 74 23
## 48 49 50 51 52 53 54 55 56 57 58 59
## 124 26 38 24 36 20 45 23 52 13 19 10
## 60 61 62 63 64 65 66 67 68 69 70 71
## 41 8 11 27 54 18 17 13 19 9 19 11
## 72 73 74 75 76 77 78 79 80 81 82 83
## 34 5 10 15 11 12 19 9 26 6 7 7
## 84 85 86 87 88 89 90 91 92 93 94 95
## 16 8 5 7 15 5 15 7 16 8 7 6
## 96 97 98 99 100 101 102 103 104 105 106 107
## 17 6 4 10 6 3 4 5 4 5 8 4
## 108 109 110 111 112 113 114 115 116 117 118 119
## 7 3 3 4 9 6 2 1 3 2 4 4
## 120 121 122 123 124 125 126 127 128 129 130 131
## 6 7 2 3 1 1 6 3 2 1 9 2
## 132 133 134 135 136 137 138 139 140 142 143 144
## 2 2 4 3 5 4 2 2 5 3 2 3
## 145 146 147 148 149 150 151 152 153 154 155 156
## 3 3 1 1 1 1 3 4 1 1 1 1
## 157 158 160 161 162 163 164 165 166 167 168 169
## 1 1 1 2 1 1 2 2 1 3 1 1
## 170 171 175 177 178 180 181 183 188 190 194 195
## 1 1 2 1 1 1 1 1 2 2 2 2
## 198 199 200 202 204 205 207 208 213 214 216 219
## 1 1 2 1 1 2 1 2 1 1 2 1
## 222 224 225 228 233 235 261 269 290 305 308 557
## 1 1 1 1 1 1 1 1 1 1 1 1here we observe that 83% are 0 values.
finding the less than value 1 & 10
NROW(data[which(data$x221<=1),])## [1] 85595here we observed 85% less than 1
NROW(data[which(data$x221<=10),])## [1] 93170here we observed 93% are less than 10
Now visualize the statistics
Histogram
hist(data$x221,
main = "Histogram of x221",
xlab = "x221")
abline(v =mean(data$x221, na.rm = TRUE),col = "red",
lwd = 2)
## observe that Approx 0 to 50 has more frequnecy than others.
Boxplot
boxplot(data$x221,
main = toupper("Boxplot of x221"),
ylab = "x221",
col = "red")
## Due to median & Quartiles are 0 it shows like a line. ##Density Plot
d <- density(data$x221, na.rm = TRUE)
plot(d, main = "Kernel density of x221")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x221, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values are lies in b0 to 50 and the skewness is a postive because the tail will have the large values. so the values are descrete data type.
summary of the variable x222
summary(data$x222)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.75 1.00 0.85 1.00 1.00 36987observe that values from 0 to 1, median is 1 & mean is 0.85
Description of the variable x222
describe(data$x222)## data$x222
## n missing distinct Info Mean Gmd .05 .10
## 63013 36987 171 0.71 0.8478 0.2378 0.00 0.50
## .25 .50 .75 .90 .95
## 0.75 1.00 1.00 1.00 1.00
##
## lowest : 0.0000 0.0869 0.0909 0.1000 0.1071, highest: 0.9677 0.9696 0.9705 0.9722 1.0000here the skewness is -2% so this negative skewness .
Total number of values excluding NA
NROW(data$x222) - NROW(data[which(is.na(data$x222)),])## [1] 63013## Here the value is 63013 because here missing values are 37%.table(data$x222)##
## 0 0.0869 0.0909 0.1 0.1071 0.1111 0.125 0.1333 0.1428 0.1499
## 3714 1 1 1 1 1 3 1 9 1
## 0.1538 0.1666 0.2 0.2222 0.2352 0.2452 0.25 0.2727 0.2857 0.2941
## 1 13 49 5 1 1 196 4 24 1
## 0.2999 0.3125 0.3181 0.3333 0.3499 0.3571 0.3611 0.3636 0.375 0.3793
## 3 1 1 738 2 2 2 7 27 1
## 0.3846 0.3888 0.4 0.4074 0.4117 0.4126 0.4166 0.423 0.4285 0.4333
## 3 1 187 1 1 1 6 1 54 1
## 0.4375 0.4444 0.45 0.4545 0.4615 0.4666 0.4736 0.4782 0.4838 0.4864
## 1 16 2 9 5 1 1 1 1 1
## 0.5 0.5238 0.5294 0.5333 0.5365 0.5384 0.5454 0.55 0.5555 0.5625
## 3903 2 4 8 1 8 10 3 36 3
## 0.5652 0.5714 0.5789 0.5833 0.5882 0.5909 0.5952 0.5999 0.6086 0.6111
## 1 144 1 11 2 1 1 563 1 2
## 0.6153 0.6166 0.625 0.6285 0.6315 0.6363 0.6428 0.647 0.65 0.6521
## 8 1 93 1 1 15 10 1 3 1
## 0.6571 0.6607 0.6666 0.68 0.6842 0.6875 0.6896 0.6923 0.6956 0.6969
## 1 1 3226 1 4 3 1 15 1 1
## 0.6999 0.7027 0.7058 0.7083 0.7142 0.7222 0.7272 0.7307 0.7333 0.7368
## 66 1 2 2 324 5 48 2 9 1
## 0.7391 0.75 0.7586 0.76 0.7619 0.7647 0.7692 0.7741 0.7777 0.7826
## 3 2200 1 1 2 7 22 1 175 1
## 0.7857 0.7894 0.8 0.8064 0.8095 0.8125 0.8181 0.8214 0.8235 0.826
## 22 6 1510 1 4 14 90 1 13 2
## 0.8333 0.8378 0.8399 0.8421 0.8437 0.8461 0.8499 0.8518 0.8571 0.8611
## 1057 1 1 14 2 36 4 3 699 1
## 0.8636 0.8666 0.8695 0.875 0.88 0.8809 0.8823 0.8888 0.8928 0.8947
## 3 33 5 461 1 1 14 336 2 15
## 0.8965 0.9 0.9047 0.9069 0.909 0.913 0.9166 0.92 0.923 0.9259
## 2 254 10 2 168 3 122 1 84 1
## 0.9285 0.931 0.9333 0.9375 0.9411 0.9444 0.9473 0.9499 0.9523 0.9545
## 60 3 46 44 23 22 16 14 6 5
## 0.9565 0.9583 0.9615 0.9642 0.9655 0.9666 0.9677 0.9696 0.9705 0.9722
## 6 4 4 4 1 1 1 3 1 1
## 1
## 41677## here we observe that values are in continous data.finding the less than value 1
NROW(data[which(data$x222<=1),])## [1] 63013here we observed 63% less than 1
Now visualize the statistics
Histogram
hist(data$x222,
main = "Histogram of x222",
xlab = "x222")
abline(v =mean(data$x222, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 1 has more frequnecy than others
Boxplot
boxplot(data$x222,
main = toupper("Boxplot of x222"),
ylab = "x222",
col = "red")
## Observe that outliers we can find which are out from the quartiles.
Density Plot
d <- density(data$x222, na.rm = TRUE)
plot(d, main = "Kernel density of x222")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x222, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observation:- ## here we can find the skewness is negative & 1 has more density there is 37% missing values.Thes values are continous data.
summary of the variable x223
summary(data$x223)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.78 1.00 0.85 1.00 1.00 37069observe that values from 0 to 1, median is 1 & mean is 0.85. there is 37% of missing values.
Description of the variable x223
describe(data$x223)## data$x223
## n missing distinct Info Mean Gmd .05 .10
## 62931 37069 172 0.706 0.8495 0.2358 0.0000 0.5000
## .25 .50 .75 .90 .95
## 0.7777 1.0000 1.0000 1.0000 1.0000
##
## lowest : 0.0000 0.0869 0.0909 0.1000 0.1071, highest: 0.9677 0.9696 0.9705 0.9722 1.0000Here the skewness is -1 % so it is negative .
Total number of values excluding NA
NROW(data$x223) - NROW(data[which(is.na(data$x223)),])## [1] 62931Here the value is 623931(62%) because there is 38% of missing values
table(data$x223)##
## 0 0.0869 0.0909 0.1 0.1071 0.1111 0.125 0.1333 0.1428 0.1499
## 3664 1 1 1 1 1 3 1 9 1
## 0.1538 0.1666 0.2 0.2222 0.2352 0.2452 0.25 0.2727 0.2857 0.2941
## 1 13 47 5 1 1 194 3 23 1
## 0.2999 0.3125 0.3181 0.3333 0.3499 0.3571 0.3611 0.3636 0.375 0.3793
## 4 1 1 732 2 2 2 8 23 1
## 0.3846 0.3888 0.4 0.4074 0.4117 0.4126 0.4166 0.423 0.4285 0.4333
## 3 1 183 1 1 1 5 1 58 1
## 0.4375 0.4444 0.45 0.4545 0.4615 0.4666 0.4736 0.4782 0.4838 0.4864
## 1 16 2 10 5 1 1 1 1 1
## 0.5 0.5238 0.5294 0.5333 0.5365 0.5384 0.5454 0.55 0.5555 0.5625
## 3857 2 4 7 1 7 10 3 35 3
## 0.5652 0.5714 0.5789 0.5833 0.5882 0.5909 0.5952 0.5999 0.6086 0.6111
## 1 142 1 11 2 1 1 552 1 2
## 0.6153 0.6166 0.625 0.6285 0.6315 0.6363 0.6428 0.647 0.65 0.6521
## 8 1 93 1 1 15 10 1 3 1
## 0.6571 0.6607 0.6666 0.68 0.6842 0.6875 0.6896 0.6923 0.6956 0.6969
## 1 1 3197 1 4 3 1 15 1 1
## 0.6999 0.7027 0.7058 0.7083 0.7142 0.7222 0.7272 0.7307 0.7333 0.7368
## 62 1 3 2 320 5 50 2 8 2
## 0.7391 0.75 0.7586 0.76 0.7619 0.7647 0.7692 0.7741 0.7777 0.7826
## 3 2169 1 1 1 7 20 1 170 1
## 0.7857 0.7894 0.7916 0.8 0.8064 0.8095 0.8125 0.8181 0.8214 0.8235
## 23 6 1 1493 1 4 14 87 1 14
## 0.826 0.8333 0.8378 0.8399 0.8421 0.8437 0.8461 0.8499 0.8518 0.8571
## 2 1058 1 1 15 2 36 4 4 677
## 0.8611 0.8636 0.8666 0.8695 0.875 0.88 0.8809 0.8823 0.8888 0.8928
## 1 3 33 5 461 1 1 13 334 2
## 0.8947 0.8965 0.9 0.9047 0.9069 0.909 0.913 0.9166 0.92 0.923
## 13 2 257 9 2 168 2 124 1 83
## 0.9259 0.9285 0.931 0.9333 0.9375 0.9411 0.9444 0.9473 0.9499 0.9523
## 1 62 3 45 42 23 22 16 13 5
## 0.9545 0.9565 0.9583 0.9615 0.9642 0.9655 0.9666 0.9677 0.9696 0.9705
## 4 6 4 4 4 1 1 1 3 1
## 0.9722 1
## 1 41837here we observe that 0 to 1 in the floating values so it is the continous.
finding the less than value 1
NROW(data[which(data$x223<=1),])## [1] 62931here we observed 62% less than 1
Now visualize the statistics
Histogram
hist(data$x223,
main = "Histogram of x223",
xlab = "x223")
abline(v =mean(data$x223, na.rm = TRUE),col = "red",
lwd = 2)
##observe that 1 has more frequnecy
Boxplot
boxplot(data$x223,
main = toupper("Boxplot of x223"),
ylab = "x223",
col = "red")
## We can find here outliers which out of the quartiles. ##Density Plot
d <- density(data$x223, na.rm = TRUE)
plot(d, main = "Kernel density of x223")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x223, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 1 has more density there is 38% missing values.values are lies b0 to 1 and the values increases from left to right which negative skewness.we found values in folating point so the values are continous data.
summary of the variable x224
summary(data$x224)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.486 2.000 37.000observe that values from 0 to 37, median is 0 & mean is 1.48
Description of the variable x224
describe(data$x224)## data$x224
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 33 0.856 1.486 2.103 0 0
## .25 .50 .75 .90 .95
## 0 0 2 4 6
##
## lowest : 0 1 2 3 4, highest: 28 29 31 33 37Here the kurtosis is low positive and the skewness is 3%.
Total number of values excluding NA
NROW(data$x224) - NROW(data[which(is.na(data$x224)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x224)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 51847 14444 11101 7850 5128 3307 2144 1408 877 581 383 269
## 12 13 14 15 16 17 18 19 20 21 22 23
## 205 138 95 56 46 32 22 15 7 8 6 5
## 24 25 26 27 28 29 31 33 37
## 4 6 6 2 2 3 1 1 1here we observe 50% of values are 0.
finding the less than value 3 & 15
NROW(data[which(data$x224<=3),])## [1] 85242here we observed 85% less than 3
NROW(data[which(data$x224<=15),])## [1] 99833here we observed 99% are less than 15
Now visualize the statistics
Histogram
hist(data$x224,
main = "Histogram of x224",
xlab = "x224")
abline(v =mean(data$x224, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10 we find the max values.
Boxplot
boxplot(data$x224,
main = toupper("Boxplot of x224"),
ylab = "x224",
col = "red")
## here the min& Q1 is 0 so its display like single line & we also got the outliers. ##Density Plot
d <- density(data$x224, na.rm = TRUE)
plot(d, main = "Kernel density of x224")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x224, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the skewness is a postive because the tail will have the large values. so the varible has descrete values.
summary of the variable x225
summary(data$x225)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.184 2.000 25.000observe that values from 0 to 25, median is 0 & mean is 1.184
Description of the variable x225
describe(data$x225)## data$x225
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 26 0.809 1.184 1.734 0 0
## .25 .50 .75 .90 .95
## 0 0 2 4 5
##
## lowest : 0 1 2 3 4, highest: 21 22 23 24 25Here the kurtosis is positive so the large data in tails.
Total number of values excluding NA
NROW(data$x225) - NROW(data[which(is.na(data$x225)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x225)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 57130 14377 10485 6986 4240 2617 1658 946 534 358 254 135
## 12 13 14 15 16 17 18 19 20 21 22 23
## 103 60 36 21 14 8 4 10 8 5 2 5
## 24 25
## 3 1here we observe that 50% of values are 0.
finding the less than value 2 & 10
NROW(data[which(data$x225<=2),])## [1] 81992## here we observed 81% less than 2.NROW(data[which(data$x225<=10),])## [1] 99585## here we observed 99% are less than 10Now visualize the statistics
Histogram
hist(data$x225,
main = "Histogram of x225",
xlab = "x225")
abline(v =mean(data$x225, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 7 has more frequnecy. ## Boxplot
boxplot(data$x225,
main = toupper("Boxplot of x225"),
ylab = "x225",
col = "red")
## we found the outliers. ##Density Plot
d <- density(data$x225, na.rm = TRUE)
plot(d, main = "Kernel density of x225")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x225, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values lies in b0 to 10 and the skewness is a postive because the tail will have the large values.so the variable has continous values.
summary of the variable x226
summary(data$x226)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.9911 1.0000 22.0000observe that values from 0 to 22, median is 0 & mean is 0.9911
Description of the variable x226
describe(data$x226)## data$x226
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 23 0.767 0.9911 1.492 0 0
## .25 .50 .75 .90 .95
## 0 0 1 3 4
##
## lowest : 0 1 2 3 4, highest: 18 19 20 21 22Here the kurtosis is positive so it has large data in tails.
Total number of values excluding NA
NROW(data$x226) - NROW(data[which(is.na(data$x226)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x226)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 61209 14066 9848 6237 3712 2070 1208 672 378 223 143 87
## 12 13 14 15 16 17 18 19 20 21 22
## 54 29 15 20 5 4 8 6 2 2 2here we observe that 61% of values are 0.
finding the less than value 1 & 8
NROW(data[which(data$x226<=1),])## [1] 75275here we observed 75% less than 1
NROW(data[which(data$x226<=8),])## [1] 99400here we observed 95% are less than 5
Now visualize the statistics
Histogram
hist(data$x226,
main = "Histogram of x226",
xlab = "x226")
abline(v =mean(data$x226, na.rm = TRUE),col = "red",
lwd = 2)
##observe that 0 to 8 we can see max values.
Boxplot
boxplot(data$x226,
main = toupper("Boxplot of x226"),
ylab = "x226",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x226, na.rm = TRUE)
plot(d, main = "Kernel density of x226")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x226, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values lies in b0 to 8 and ,the skewness is a postive because the tail will have the large values.so the variable has descrete values.
summary of the variable x227
summary(data$x227)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7511 1.0000 20.0000observe that values from 0 to 20, meadian is 0 & mean is 0.7511
Description of the variable x227
describe(data$x227)## data$x227
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 21 0.696 0.7511 1.177 0 0
## .25 .50 .75 .90 .95
## 0 0 1 3 4
##
## lowest : 0 1 2 3 4, highest: 16 17 18 19 20Here the kurtosis is positive so it has a large data value in tails.
Total number of values excluding NA
NROW(data$x227) - NROW(data[which(is.na(data$x227)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x227)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 67007 13494 8786 5047 2753 1373 759 354 195 97 47 39
## 12 13 14 15 16 17 18 19 20
## 25 9 4 1 2 4 1 1 2here we observe that 67% are 0 values.
finding the less than value 1 & 7
NROW(data[which(data$x227<=1),])## [1] 80501here we observed 80% less than 1
NROW(data[which(data$x227<=5),])## [1] 98460here we observed 98% are less than 5
Now visualize the statistics
Histogram
hist(data$x227,
main = "Histogram of x227",
xlab = "x227")
abline(v =mean(data$x227, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 9 has max values.
Boxplot
boxplot(data$x227,
main = toupper("Boxplot of x227"),
ylab = "x227",
col = "red")
## we found the outliers
Density Plot
d <- density(data$x227, na.rm = TRUE)
plot(d, main = "Kernel density of x227")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x227, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values aree lies in b0 to 8 and the skewness is a postive because the tail will have the large values. so the variable has descrete values.
summary of the variable x228
summary(data$x228)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4134 0.0000 12.0000observe that values from 0 to 12, meadian is 0 & mean is 0.4134
Description of the variable x228
describe(data$x228)## data$x228
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 13 0.534 0.4134 0.6961 0 0
## .25 .50 .75 .90 .95
## 0 0 0 2 2
##
## Value 0 1 2 3 4 5 6 7 8 9
## Frequency 77429 11736 6100 2811 1183 433 186 61 36 13
## Proportion 0.774 0.117 0.061 0.028 0.012 0.004 0.002 0.001 0.000 0.000
##
## Value 10 11 12
## Frequency 7 4 1
## Proportion 0.000 0.000 0.000Here the kurtosis is positive so it has large data value in tails.
Total number of values excluding NA
NROW(data$x228) - NROW(data[which(is.na(data$x228)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x228)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 77429 11736 6100 2811 1183 433 186 61 36 13 7 4
## 12
## 1here we observe that 77% are 0 and its lokk like categorical
finding the less than value 2 & 9
NROW(data[which(data$x228<2),])## [1] 89165here we observed 89% is less than 2
NROW(data[which(data$x228<=9),])## [1] 99988here we observed 99% are less than 9
Now visualize the statistics
Histogram
hist(data$x228,
main = "Histogram of x228",
xlab = "x228")
abline(v =mean(data$x228, na.rm = TRUE),col = "red",
lwd = 2)
##we clearly observe that from 0 to 7 intervals of frequency.
Boxplot
boxplot(data$x228,
main = toupper("Boxplot of x228"),
ylab = "x228",
col = "red")
## Due to the median & quartiles are 0 its shows like line .
Density Plot
d <- density(data$x228, na.rm = TRUE)
plot(d, main = "Kernel density of x228")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x228, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values are lies in b0 to 7 and the skewness is a postive because the tail will have the large values.so the variable has categorical values.
summary of the variable x229
summary(data$x229)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2463 0.0000 10.0000observe that values from 0 to 10, median is 0 & mean is 0.2463
Description of the variable x229
describe(data$x229)## data$x229
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 11 0.4 0.2463 0.4355 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 2
##
## Value 0 1 2 3 4 5 6 7 8 9
## Frequency 84298 9844 3779 1406 460 143 43 21 3 2
## Proportion 0.843 0.098 0.038 0.014 0.005 0.001 0.000 0.000 0.000 0.000
##
## Value 10
## Frequency 1
## Proportion 0.000Here the kurtosis is positive so it has large data value in tails.
Total number of values excluding NA
NROW(data$x229) - NROW(data[which(is.na(data$x229)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x229)##
## 0 1 2 3 4 5 6 7 8 9 10
## 84298 9844 3779 1406 460 143 43 21 3 2 1here we observe that 84 % are 0 and also it has only 10 values this is like categorical variable.
finding the less than value 1 & 5
NROW(data[which(data$x229<=1),])## [1] 94142here we observed 94% less than 1
NROW(data[which(data$x229<=5),])## [1] 99930here we observed 99% are less than 5
Now visualize the statistics
Histogram
hist(data$x229,
main = "Histogram of x229",
xlab = "x229")
abline(v =mean(data$x229, na.rm = TRUE),col = "red",
lwd = 2)
##observe that data intervals its repersents a categorical data.
Boxplot
boxplot(data$x229,
main = toupper("Boxplot of x229"),
ylab = "x229",
col = "red")
## Obsever that the median & quartiles ar 0 so it shows like a line ,we also find the outliers.
Density Plot
d <- density(data$x229, na.rm = TRUE)
plot(d, main = "Kernel density of x229")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x229, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values are lies in b0 to 5 and the skewness is a postive because the tail will have the large values.Here variables has the categorical values.
summary of the variable x230
summary(data$x230)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1929 0.0000 26.0000observe that values from 0 to 26, median is 0 & mean is 0.1929
Description of the variable x230
describe(data$x230)## data$x230
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 20 0.346 0.1929 0.348 0 0
## .25 .50 .75 .90 .95
## 0 0 0 1 1
##
## Value 0 1 2 3 4 5 6 7 8 9
## Frequency 86745 9846 2184 663 264 109 81 38 19 15
## Proportion 0.867 0.098 0.022 0.007 0.003 0.001 0.001 0.000 0.000 0.000
##
## Value 10 11 12 13 14 15 17 18 21 26
## Frequency 12 8 2 6 3 1 1 1 1 1
## Proportion 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Here the kurtosis is positive so it has large data in tails.
Total number of values excluding NA
NROW(data$x230) - NROW(data[which(is.na(data$x230)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x230)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 86745 9846 2184 663 264 109 81 38 19 15 12 8
## 12 13 14 15 17 18 21 26
## 2 6 3 1 1 1 1 1here we observe that 86% of values are 0.
finding the less than value 1 & 3
NROW(data[which(data$x230<=1),])## [1] 96591here we observed 96% less than 1
NROW(data[which(data$x230<=3),])## [1] 99438here we observed 99% are less than 3
Now visualize the statistics
Histogram
hist(data$x230,
main = "Histogram of x230",
xlab = "x230")
abline(v =mean(data$x230, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 7 has max values.
Boxplot
boxplot(data$x230,
main = toupper("Boxplot of x230"),
ylab = "x230",
col = "red")
## Due to the median & Quartiles are 0 so its shows like a lin in the graph.
Density Plot
d <- density(data$x230, na.rm = TRUE)
plot(d, main = "Kernel density of x230")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x230, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 3 and the skewness is a postive because the tail will have the large values. so the variable has a descrete values.
summary of the variable x231
summary(data$x231)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4714 1.0000 43.0000observe that values from 0 to 43, median is 0 & mean is 0.4714
Description of the variable x231
describe(data$x231)## data$x231
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 32 0.593 0.4714 0.781 0 0
## .25 .50 .75 .90 .95
## 0 0 1 1 2
##
## lowest : 0 1 2 3 4, highest: 30 33 39 40 43Here the kurtosis is positive and it has large data in tails.
Total number of values excluding NA
NROW(data$x231) - NROW(data[which(is.na(data$x231)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x231)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 73857 16321 5367 2095 979 528 269 184 136 84 44 31
## 12 13 14 15 16 17 18 19 20 21 22 23
## 24 17 12 10 11 7 3 1 1 7 1 3
## 24 26 27 30 33 39 40 43
## 1 1 1 1 1 1 1 1here we observe that 73% of values are 0.
finding the less than value 1 & 10
NROW(data[which(data$x231<=1),])## [1] 90178here we observed 90% less than 1
NROW(data[which(data$x231<=10),])## [1] 99864here we observed 99% are less than 10
Now visualize the statistics
##Histogram
hist(data$x231,
main = "Histogram of x231",
xlab = "x231")
abline(v =mean(data$x231, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10 has a max values.
Boxplot
boxplot(data$x231,
main = toupper("Boxplot of x231"),
ylab = "x231",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x231, na.rm = TRUE)
plot(d, main = "Kernel density of x231")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x231, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 5 and the skewness is a postive because the tail will have the large values.so the variable has descrete values.
summary of the variable x232
summary(data$x232)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.9162 1.0000 101.0000observe that values from 0 to 101, median is 0 & mean is 0.9162
Description of the variable x232
describe(data$x232)## data$x232
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 44 0.774 0.9162 1.397 0 0
## .25 .50 .75 .90 .95
## 0 0 1 3 4
##
## lowest : 0 1 2 3 4, highest: 44 52 55 60 101Here the kurtosis is positive and it has large data in tails.
Total number of values excluding NA
NROW(data$x232) - NROW(data[which(is.na(data$x232)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x232)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 60072 20075 9013 4415 2443 1327 792 533 369 234 182 126
## 12 13 14 15 16 17 18 19 20 21 22 23
## 64 69 58 47 30 27 29 15 11 10 12 9
## 24 25 26 27 28 29 30 31 33 36 37 39
## 4 7 3 2 2 4 1 2 1 1 1 1
## 40 42 43 44 52 55 60 101
## 1 1 1 2 1 1 1 1here we observe that 60% of values are 0.
finding the less than value 1 & 10
NROW(data[which(data$x232<=1),])## [1] 80147## here we observed 80% less than 1NROW(data[which(data$x232<=10),])## [1] 99455## here we observed 99% are less than 10Now visualize the statistics
##Histogram
hist(data$x232,
main = "Histogram of x232",
xlab = "x232")
abline(v =mean(data$x232, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 20 has max value.
Boxplot
boxplot(data$x232,
main = toupper("Boxplot of x232"),
ylab = "x232",
col = "red")
## here we got the outliers value.
Density Plot
d <- density(data$x232, na.rm = TRUE)
plot(d, main = "Kernel density of x232")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x232, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the skewness is a postive because the tail will have the large values so the variable has a descrete values.
summary of the variable x233
summary(data$x233)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 983 4714 4770 788263observe that values from 0 to 788263, meadian is 4714 & mean is 4714
Description of the variable x233
describe(data$x233)## data$x233
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 19427 0.975 4714 7199 0 0
## .25 .50 .75 .90 .95
## 0 983 4770 13072 21701
##
## lowest : 0 1 2 3 4, highest: 254751 282391 478615 572688 788263Here the kurtosis is positive and it has large dat in tails.
Total number of values excluding NA
NROW(data$x233) - NROW(data[which(is.na(data$x233)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x233)##
## 0 1 2 3 4 5 6 7 8 9
## 29349 18 22 17 20 19 22 13 22 34
## 10 11 12 13 14 15 16 17 18 19
## 20 20 23 14 24 23 21 18 15 24
## 20 21 22 23 24 25 26 27 28 29
## 28 14 12 13 21 35 16 24 18 27
## 30 31 32 33 34 35 36 37 38 39
## 22 16 20 30 16 19 13 15 19 26
## 40 41 42 43 44 45 46 47 48 49
## 32 19 20 15 19 24 24 18 19 29
## 50 51 52 53 54 55 56 57 58 59
## 31 23 23 17 27 25 21 15 21 28
## 60 61 62 63 64 65 66 67 68 69
## 17 16 17 17 20 19 17 22 12 19
## 70 71 72 73 74 75 76 77 78 79
## 18 11 17 25 30 46 23 18 23 20
## 80 81 82 83 84 85 86 87 88 89
## 19 26 18 18 11 18 33 34 14 20
## 90 91 92 93 94 95 96 97 98 99
## 25 16 11 16 19 27 19 28 23 18
## 100 101 102 103 104 105 106 107 108 109
## 34 28 18 21 17 28 20 28 27 24
## 110 111 112 113 114 115 116 117 118 119
## 16 23 21 17 23 12 22 12 25 14
## 120 121 122 123 124 125 126 127 128 129
## 20 26 16 28 16 21 26 13 15 25
## 130 131 132 133 134 135 136 137 138 139
## 23 17 23 21 32 22 16 16 21 25
## 140 141 142 143 144 145 146 147 148 149
## 16 19 24 20 22 19 23 20 15 25
## 150 151 152 153 154 155 156 157 158 159
## 25 20 23 15 19 31 14 31 17 19
## 160 161 162 163 164 165 166 167 168 169
## 24 21 23 20 17 15 9 24 14 25
## 170 171 172 173 174 175 176 177 178 179
## 18 23 14 21 17 27 23 16 22 16
## 180 181 182 183 184 185 186 187 188 189
## 17 22 25 19 20 19 21 20 24 23
## 190 191 192 193 194 195 196 197 198 199
## 18 26 18 19 20 27 22 21 22 27
## 200 201 202 203 204 205 206 207 208 209
## 31 18 18 21 29 19 20 23 23 23
## 210 211 212 213 214 215 216 217 218 219
## 21 19 23 22 14 17 20 6 25 22
## 220 221 222 223 224 225 226 227 228 229
## 19 15 21 24 24 25 24 10 19 19
## 230 231 232 233 234 235 236 237 238 239
## 17 24 14 29 18 15 20 24 16 18
## 240 241 242 243 244 245 246 247 248 249
## 22 24 28 22 18 21 30 30 16 23
## 250 251 252 253 254 255 256 257 258 259
## 27 13 23 36 23 34 21 19 24 25
## 260 261 262 263 264 265 266 267 268 269
## 23 18 15 19 21 23 23 23 27 21
## 270 271 272 273 274 275 276 277 278 279
## 21 26 24 17 25 22 28 24 31 28
## 280 281 282 283 284 285 286 287 288 289
## 24 30 19 28 19 26 23 18 28 24
## 290 291 292 293 294 295 296 297 298 299
## 21 18 26 23 19 20 29 31 42 25
## 300 301 302 303 304 305 306 307 308 309
## 33 30 31 39 32 29 26 20 20 27
## 310 311 312 313 314 315 316 317 318 319
## 20 23 19 24 19 19 25 21 22 32
## 320 321 322 323 324 325 326 327 328 329
## 23 15 15 23 18 26 21 15 21 24
## 330 331 332 333 334 335 336 337 338 339
## 26 26 32 25 22 34 22 21 18 39
## 340 341 342 343 344 345 346 347 348 349
## 18 23 23 28 17 18 19 21 22 19
## 350 351 352 353 354 355 356 357 358 359
## 24 21 19 19 22 26 19 24 20 19
## 360 361 362 363 364 365 366 367 368 369
## 24 24 16 24 23 31 20 21 19 27
## 370 371 372 373 374 375 376 377 378 379
## 29 20 35 21 17 17 23 26 27 25
## 380 381 382 383 384 385 386 387 388 389
## 31 30 28 34 34 21 29 19 26 25
## 390 391 392 393 394 395 396 397 398 399
## 17 33 26 50 56 35 28 34 30 31
## 400 401 402 403 404 405 406 407 408 409
## 36 42 19 26 27 30 25 21 25 33
## 410 411 412 413 414 415 416 417 418 419
## 31 31 28 36 23 27 29 42 33 35
## 420 421 422 423 424 425 426 427 428 429
## 38 45 46 45 62 62 59 35 45 50
## 430 431 432 433 434 435 436 437 438 439
## 57 52 66 51 54 47 41 40 42 30
## 440 441 442 443 444 445 446 447 448 449
## 45 42 47 47 40 46 47 34 32 44
## 450 451 452 453 454 455 456 457 458 459
## 44 45 36 38 38 27 38 29 30 31
## 460 461 462 463 464 465 466 467 468 469
## 45 25 26 27 33 29 27 28 27 29
## 470 471 472 473 474 475 476 477 478 479
## 22 20 25 29 27 21 34 18 27 34
## 480 481 482 483 484 485 486 487 488 489
## 20 17 30 30 22 27 35 13 23 16
## 490 491 492 493 494 495 496 497 498 499
## 27 21 34 17 26 28 26 17 25 36
## 500 501 502 503 504 505 506 507 508 509
## 37 28 30 24 24 24 36 26 27 25
## 510 511 512 513 514 515 516 517 518 519
## 30 19 15 14 23 23 21 23 24 17
## 520 521 522 523 524 525 526 527 528 529
## 15 23 16 27 18 24 22 23 27 18
## 530 531 532 533 534 535 536 537 538 539
## 19 24 26 19 31 19 25 24 32 30
## 540 541 542 543 544 545 546 547 548 549
## 22 20 32 21 29 19 23 26 28 13
## 550 551 552 553 554 555 556 557 558 559
## 20 35 26 25 15 26 26 19 18 28
## 560 561 562 563 564 565 566 567 568 569
## 28 15 23 18 17 17 24 26 10 22
## 570 571 572 573 574 575 576 577 578 579
## 19 18 20 24 26 14 20 14 18 23
## 580 581 582 583 584 585 586 587 588 589
## 16 25 22 19 27 16 15 25 18 27
## 590 591 592 593 594 595 596 597 598 599
## 17 19 21 25 26 20 20 11 25 28
## 600 601 602 603 604 605 606 607 608 609
## 21 26 15 20 23 12 15 23 14 18
## 610 611 612 613 614 615 616 617 618 619
## 15 15 15 15 22 9 16 18 17 16
## 620 621 622 623 624 625 626 627 628 629
## 21 10 13 16 16 21 13 17 12 18
## 630 631 632 633 634 635 636 637 638 639
## 20 15 15 21 22 19 15 21 20 12
## 640 641 642 643 644 645 646 647 648 649
## 25 18 14 20 19 21 18 16 18 16
## 650 651 652 653 654 655 656 657 658 659
## 14 10 15 15 14 18 18 13 12 24
## 660 661 662 663 664 665 666 667 668 669
## 9 33 13 15 11 19 15 8 18 19
## 670 671 672 673 674 675 676 677 678 679
## 21 16 15 15 14 15 25 14 17 15
## 680 681 682 683 684 685 686 687 688 689
## 15 15 22 13 17 16 17 14 16 18
## 690 691 692 693 694 695 696 697 698 699
## 16 14 18 15 15 13 15 17 16 12
## 700 701 702 703 704 705 706 707 708 709
## 17 17 18 18 15 18 23 15 12 14
## 710 711 712 713 714 715 716 717 718 719
## 23 16 14 15 16 22 16 21 12 19
## 720 721 722 723 724 725 726 727 728 729
## 10 10 11 12 14 20 12 20 12 15
## 730 731 732 733 734 735 736 737 738 739
## 12 11 9 22 11 15 21 18 13 16
## 740 741 742 743 744 745 746 747 748 749
## 20 23 18 21 18 17 20 13 13 17
## 750 751 752 753 754 755 756 757 758 759
## 16 15 10 14 16 12 19 23 18 21
## 760 761 762 763 764 765 766 767 768 769
## 11 19 22 8 12 14 11 10 17 24
## 770 771 772 773 774 775 776 777 778 779
## 11 14 16 19 12 12 14 18 16 21
## 780 781 782 783 784 785 786 787 788 789
## 20 23 17 16 17 19 15 19 13 10
## 790 791 792 793 794 795 796 797 798 799
## 21 20 16 13 23 22 10 13 10 16
## 800 801 802 803 804 805 806 807 808 809
## 17 10 7 15 16 18 19 17 16 15
## 810 811 812 813 814 815 816 817 818 819
## 19 16 16 19 18 13 13 22 21 12
## 820 821 822 823 824 825 826 827 828 829
## 10 28 13 22 16 17 13 12 16 13
## 830 831 832 833 834 835 836 837 838 839
## 15 19 22 9 23 7 18 14 12 19
## 840 841 842 843 844 845 846 847 848 849
## 11 19 10 7 20 13 10 20 17 26
## 850 851 852 853 854 855 856 857 858 859
## 24 23 12 16 19 23 14 16 18 14
## 860 861 862 863 864 865 866 867 868 869
## 16 15 19 13 13 12 13 16 10 20
## 870 871 872 873 874 875 876 877 878 879
## 17 12 23 17 16 17 20 13 19 20
## 880 881 882 883 884 885 886 887 888 889
## 11 11 22 15 10 19 16 19 25 15
## 890 891 892 893 894 895 896 897 898 899
## 13 18 17 20 20 17 19 11 17 18
## 900 901 902 903 904 905 906 907 908 909
## 21 24 16 19 13 13 17 19 8 15
## 910 911 912 913 914 915 916 917 918 919
## 18 14 10 13 11 19 17 19 11 18
## 920 921 922 923 924 925 926 927 928 929
## 15 22 13 19 15 15 19 17 8 20
## 930 931 932 933 934 935 936 937 938 939
## 19 12 14 14 16 18 13 19 13 6
## 940 941 942 943 944 945 946 947 948 949
## 12 21 14 12 8 15 14 21 19 19
## 950 951 952 953 954 955 956 957 958 959
## 24 18 18 12 14 12 13 13 7 11
## 960 961 962 963 964 965 966 967 968 969
## 9 22 9 13 24 12 16 13 18 16
## 970 971 972 973 974 975 976 977 978 979
## 14 13 9 15 12 15 9 17 15 17
## 980 981 982 983 984 985 986 987 988 989
## 16 13 15 17 24 15 12 15 20 14
## 990 991 992 993 994 995 996 997 998 999
## 17 9 18 17 20 19 15 8 12 19
## 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009
## 18 16 12 9 20 14 16 13 12 16
## 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019
## 12 14 15 15 18 27 16 15 20 19
## 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029
## 9 17 20 15 19 16 18 22 11 6
## 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039
## 12 7 17 11 13 16 10 11 15 10
## 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
## 16 8 17 21 13 16 11 13 21 15
## 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
## 16 10 14 13 9 15 10 10 9 10
## 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069
## 11 18 16 13 16 17 12 15 17 8
## 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079
## 15 15 20 18 10 10 16 20 20 14
## 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089
## 9 15 12 13 10 25 15 14 11 15
## 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099
## 14 10 13 12 14 14 9 8 8 15
## 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109
## 10 12 12 13 12 13 20 9 14 7
## 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
## 12 15 13 13 15 12 9 13 9 22
## 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129
## 14 12 21 15 9 12 15 8 10 7
## 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139
## 18 18 5 13 14 10 8 11 14 11
## 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149
## 19 7 16 14 12 13 10 15 12 18
## 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159
## 7 5 8 10 9 10 13 11 10 8
## 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169
## 18 13 10 14 10 16 12 14 11 8
## 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179
## 10 17 14 12 14 12 12 12 10 12
## 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
## 15 10 12 13 10 20 10 8 15 10
## 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199
## 18 17 8 7 9 9 8 16 12 12
## 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209
## 15 7 11 18 12 12 10 14 14 14
## 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219
## 13 17 6 11 16 13 10 10 14 12
## 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229
## 12 6 8 11 14 13 10 11 9 10
## 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239
## 8 14 18 17 10 11 20 8 9 13
## 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249
## 9 13 8 9 13 12 9 9 12 9
## 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
## 10 9 14 15 16 12 12 14 17 14
## 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269
## 16 12 6 12 7 7 9 14 10 21
## 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279
## 13 12 11 9 8 11 9 16 12 13
## 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289
## 8 13 19 12 10 13 19 9 12 5
## 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299
## 11 12 12 7 12 17 9 6 14 11
## 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309
## 17 10 11 9 9 11 6 11 9 11
## 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319
## 14 19 8 6 9 8 12 13 10 13
## 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329
## 15 13 12 9 16 11 8 15 11 8
## 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339
## 8 11 16 5 7 14 10 5 12 22
## 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349
## 16 16 10 9 10 4 13 11 12 17
## 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359
## 9 16 12 15 8 7 13 13 16 6
## 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369
## 10 13 17 12 11 10 9 7 13 9
## 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379
## 4 12 7 9 13 7 18 9 11 10
## 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389
## 5 12 8 8 11 10 11 14 5 7
## 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399
## 4 13 8 9 6 8 9 8 10 5
## 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409
## 17 13 15 6 14 14 10 5 11 15
## 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419
## 11 14 7 13 17 13 6 12 8 10
## 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429
## 9 16 9 10 11 9 10 5 11 18
## 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439
## 11 14 19 8 9 13 16 9 12 10
## 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449
## 13 19 15 9 10 9 6 8 10 9
## 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459
## 9 14 6 12 13 8 9 11 13 12
## 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469
## 16 16 7 6 13 12 15 9 3 16
## 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479
## 10 7 11 6 19 17 12 5 10 11
## 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489
## 11 6 12 7 14 9 7 9 11 5
## 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499
## 11 23 7 14 9 7 11 9 7 11
## 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509
## 12 5 13 11 12 4 11 10 8 12
## 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519
## 10 10 4 9 14 17 9 11 8 9
## 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529
## 11 15 14 7 12 6 12 12 12 6
## 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539
## 12 11 15 10 4 9 9 5 10 10
## 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549
## 8 6 14 13 9 8 10 10 5 8
## 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559
## 12 14 7 7 8 11 7 6 12 10
## 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569
## 9 15 13 9 10 12 14 5 8 13
## 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579
## 9 14 10 8 5 10 11 8 10 6
## 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589
## 14 10 11 8 6 13 10 8 7 12
## 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599
## 11 8 13 18 10 3 11 13 10 8
## 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609
## 12 8 8 8 9 7 7 6 10 12
## 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619
## 14 11 7 17 7 7 13 15 10 7
## 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629
## 10 10 9 6 7 9 8 11 10 15
## 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639
## 10 13 9 12 9 12 3 11 4 9
## 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649
## 6 11 5 9 3 9 11 10 14 11
## 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659
## 8 7 8 6 9 13 20 7 12 5
## 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669
## 8 9 8 12 6 13 10 8 11 7
## 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679
## 11 9 12 15 9 12 5 7 11 6
## 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689
## 7 3 10 7 12 9 11 11 5 10
## 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699
## 5 7 12 6 7 12 7 11 9 12
## 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709
## 10 12 7 9 8 11 5 6 11 7
## 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719
## 13 9 11 11 11 8 12 13 4 9
## 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729
## 16 8 11 11 8 7 11 9 8 8
## 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739
## 10 10 11 11 6 14 6 9 8 8
## 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749
## 8 11 10 13 5 10 8 7 12 7
## 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759
## 7 6 15 9 11 4 5 5 7 10
## 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769
## 8 8 2 13 10 10 5 8 4 7
## 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779
## 9 14 6 5 14 4 9 10 4 8
## 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789
## 16 7 7 4 12 10 5 7 8 8
## 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799
## 10 9 6 13 5 5 4 4 6 9
## 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809
## 11 13 12 8 13 6 14 10 7 5
## 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
## 6 11 13 8 7 11 4 12 11 14
## 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829
## 8 13 8 6 10 9 10 6 9 11
## 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839
## 4 6 7 7 8 10 8 3 13 13
## 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849
## 11 8 12 9 7 9 8 13 11 7
## 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859
## 5 10 6 7 4 4 11 11 11 6
## 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869
## 11 11 9 7 11 8 4 15 9 9
## 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879
## 12 8 3 7 7 5 9 9 14 8
## 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
## 9 11 13 12 9 2 9 6 6 11
## 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899
## 12 11 8 6 5 5 5 10 9 8
## 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909
## 4 14 7 6 10 5 7 8 12 9
## 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919
## 9 9 10 3 7 8 9 8 13 10
## 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929
## 12 8 12 9 6 8 8 10 9 6
## 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939
## 13 8 5 9 11 12 8 12 12 6
## 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949
## 13 8 8 13 11 6 5 4 5 10
## 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
## 10 9 9 6 8 11 9 5 8 5
## 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
## 11 11 6 10 5 6 7 11 10 8
## 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979
## 9 7 10 10 5 15 12 8 8 10
## 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
## 3 4 6 7 9 7 4 7 8 12
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## 9 7 9 9 9 6 9 9 4 13
## 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
## 17 10 13 7 6 4 6 5 6 9
## 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
## 11 5 8 7 5 10 6 7 7 3
## 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
## 8 5 11 5 5 6 11 9 9 11
## 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039
## 8 4 8 8 8 8 12 10 7 7
## 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049
## 13 6 5 4 10 10 6 9 9 7
## 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059
## 7 6 4 5 9 8 7 11 5 7
## 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069
## 8 7 3 8 6 6 6 10 10 8
## 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079
## 5 9 6 11 6 10 9 9 6 7
## 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089
## 3 6 11 9 10 5 9 6 10 1
## 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
## 11 6 6 6 11 10 6 5 10 6
## 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109
## 9 9 6 3 6 7 5 7 6 5
## 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119
## 8 8 8 2 11 8 2 3 4 6
## 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129
## 7 11 6 4 9 8 8 9 9 7
## 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139
## 5 13 9 7 7 10 3 7 12 4
## 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149
## 10 4 6 6 6 6 3 5 5 3
## 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159
## 4 6 5 7 10 6 8 10 7 7
## 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
## 10 3 9 10 4 5 8 8 5 4
## 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179
## 9 7 5 4 4 8 9 7 2 8
## 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189
## 5 8 11 7 11 8 7 7 3 3
## 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199
## 9 5 5 9 5 6 4 6 4 5
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## 9604 9605 9606 9608 9609 9610 9612 9613 9615 9617
## 1 3 2 2 3 1 2 4 1 2
## 9619 9620 9621 9622 9623 9624 9626 9628 9629 9631
## 7 2 1 2 1 2 2 2 1 1
## 9633 9635 9636 9637 9639 9641 9642 9643 9645 9646
## 3 1 1 2 1 2 1 1 2 3
## 9647 9648 9649 9650 9651 9652 9653 9654 9657 9659
## 3 1 2 1 1 1 1 1 2 1
## 9660 9661 9662 9663 9667 9668 9669 9670 9671 9673
## 1 1 1 1 2 1 1 3 1 2
## 9675 9676 9677 9679 9680 9681 9682 9685 9686 9687
## 1 2 1 1 1 2 1 4 3 3
## 9688 9689 9690 9691 9692 9693 9694 9696 9697 9698
## 1 1 2 1 1 2 2 1 4 1
## 9700 9701 9703 9704 9705 9707 9708 9709 9710 9711
## 2 2 1 2 1 2 1 3 2 4
## 9712 9713 9714 9715 9716 9717 9718 9722 9723 9725
## 2 1 1 3 2 2 2 4 2 1
## 9726 9727 9730 9731 9733 9734 9735 9736 9737 9739
## 1 1 1 2 3 1 1 2 1 2
## 9740 9742 9744 9747 9749 9750 9751 9752 9753 9756
## 3 1 4 1 1 2 2 2 2 2
## 9757 9758 9759 9760 9761 9762 9763 9765 9767 9769
## 1 2 4 1 2 1 1 2 2 3
## 9770 9771 9773 9775 9776 9777 9779 9780 9782 9783
## 1 2 5 3 2 2 1 1 2 1
## 9785 9787 9788 9790 9791 9792 9793 9794 9795 9796
## 1 2 1 3 5 1 1 1 2 1
## 9798 9799 9800 9801 9802 9803 9805 9807 9808 9809
## 1 1 1 3 2 2 1 3 2 2
## 9810 9811 9812 9813 9814 9816 9818 9823 9825 9826
## 1 1 1 2 1 2 4 1 2 2
## 9827 9829 9830 9831 9833 9834 9835 9837 9841 9843
## 2 2 2 2 4 1 2 1 1 1
## 9844 9845 9846 9847 9848 9849 9850 9852 9853 9854
## 2 1 1 2 3 2 3 1 2 2
## 9855 9856 9857 9858 9861 9862 9863 9865 9866 9867
## 1 2 1 1 2 1 2 2 2 1
## 9870 9871 9872 9874 9876 9877 9879 9880 9881 9882
## 1 1 1 3 1 1 3 2 1 2
## 9884 9885 9886 9888 9890 9891 9893 9896 9897 9898
## 2 2 1 4 2 1 1 1 1 1
## 9899 9901 9902 9903 9904 9907 9908 9909 9911 9913
## 2 1 2 1 3 1 1 4 1 4
## 9914 9919 9921 9923 9924 9926 9927 9928 9929 9930
## 1 1 1 2 1 2 1 3 2 1
## 9931 9932 9935 9936 9937 9939 9940 9941 9942 9943
## 3 1 2 1 4 2 2 2 1 1
## 9946 9947 9948 9951 9952 9954 9955 9957 9958 9960
## 3 2 3 2 1 2 2 1 1 2
## 9961 9962 9964 9966 9967 9968 9970 9971 9973 9974
## 3 3 4 2 1 1 2 1 1 2
## 9976 9978 9979 9980 9984 9985 9986 9987 9988 9989
## 2 2 1 3 2 1 2 1 2 2
## 9990 9991 9993 9994 9996 9997 9998 10000 10001 10002
## 1 4 1 4 2 1 1 2 1 1
## 10003 10004 10005 10006 10008 10009 10010 10011 10013 10014
## 4 1 2 1 5 1 1 2 1 1
## 10015 10016 10017 10018 10019 10020 10023 10024 10026 10027
## 1 1 2 2 2 2 2 2 3 3
## 10028 10030 10031 10032 10033 10034 10035 10036 10037 10038
## 3 1 3 1 1 1 1 2 1 2
## 10039 10041 10043 10045 10048 10051 10052 10053 10055 10056
## 1 1 3 2 1 1 1 1 2 3
## 10059 10060 10061 10062 10063 10064 10065 10066 10070 10071
## 3 1 1 2 1 3 1 1 4 1
## 10072 10075 10076 10077 10078 10079 10081 10082 10083 10085
## 3 2 3 4 2 5 1 3 1 1
## 10086 10088 10089 10090 10091 10093 10094 10097 10098 10099
## 2 1 2 1 1 1 2 3 1 2
## 10101 10102 10103 10104 10105 10106 10107 10109 10110 10111
## 3 1 2 1 1 1 1 1 3 4
## 10112 10113 10114 10115 10119 10120 10122 10123 10124 10125
## 4 3 2 2 2 2 1 2 1 3
## 10126 10128 10129 10130 10131 10132 10134 10135 10137 10138
## 3 1 2 1 1 1 1 3 1 4
## 10143 10144 10145 10147 10149 10151 10152 10156 10157 10158
## 1 2 1 3 6 1 2 2 2 1
## 10160 10162 10163 10164 10165 10166 10169 10170 10171 10172
## 1 2 2 3 5 2 2 2 2 1
## 10173 10174 10175 10176 10178 10179 10180 10181 10182 10184
## 1 2 2 1 2 1 1 4 2 1
## 10186 10187 10189 10191 10193 10194 10195 10196 10197 10198
## 3 4 1 1 4 2 4 1 3 1
## 10199 10200 10201 10202 10203 10205 10206 10207 10208 10209
## 2 2 5 2 1 1 3 1 1 4
## 10210 10212 10213 10214 10216 10217 10219 10220 10221 10222
## 3 2 1 2 2 2 2 2 1 1
## 10223 10224 10228 10229 10230 10231 10233 10234 10235 10236
## 1 2 2 4 3 4 1 1 2 2
## 10237 10240 10241 10242 10245 10246 10247 10248 10249 10250
## 4 1 2 1 1 5 4 1 1 4
## 10251 10254 10255 10256 10257 10259 10261 10262 10264 10267
## 2 1 3 5 1 1 1 2 2 5
## 10268 10269 10270 10271 10272 10273 10276 10278 10279 10281
## 1 1 1 2 2 2 2 1 1 1
## 10282 10283 10285 10286 10287 10289 10290 10291 10294 10296
## 1 2 1 1 2 3 1 1 1 1
## 10297 10298 10301 10302 10303 10304 10306 10307 10309 10310
## 1 3 2 1 1 1 3 1 6 1
## 10311 10313 10314 10315 10316 10317 10319 10320 10321 10322
## 3 2 2 1 2 2 2 2 3 1
## 10323 10325 10327 10328 10329 10330 10331 10332 10333 10334
## 2 2 2 1 4 3 2 1 3 2
## 10337 10338 10339 10340 10341 10344 10347 10349 10350 10351
## 1 2 3 2 2 1 2 1 3 1
## 10352 10355 10356 10357 10358 10359 10360 10363 10366 10368
## 1 1 1 2 1 1 1 1 1 1
## 10369 10370 10371 10372 10373 10374 10375 10377 10378 10380
## 1 2 2 2 2 2 1 1 2 2
## 10381 10383 10386 10387 10388 10389 10390 10391 10393 10394
## 1 4 1 1 2 2 1 2 2 1
## 10395 10397 10398 10400 10401 10402 10403 10404 10406 10408
## 1 1 1 2 3 2 1 1 2 1
## 10410 10411 10412 10413 10415 10416 10421 10423 10425 10426
## 1 1 1 1 1 3 1 3 2 1
## 10427 10429 10430 10431 10433 10436 10437 10439 10440 10443
## 1 1 1 2 1 2 2 2 3 2
## 10445 10447 10448 10450 10455 10457 10459 10460 10462 10463
## 2 1 1 1 1 5 2 1 1 6
## 10466 10469 10471 10472 10473 10475 10476 10477 10478 10479
## 2 1 3 2 1 1 2 1 2 1
## 10480 10482 10483 10485 10487 10489 10491 10492 10494 10496
## 1 3 1 2 1 1 1 1 1 2
## 10498 10499 10500 10501 10503 10504 10505 10506 10507 10508
## 3 1 2 2 3 1 1 4 1 2
## 10510 10511 10512 10514 10515 10518 10519 10520 10521 10522
## 3 1 3 2 2 1 1 3 2 1
## 10523 10524 10525 10526 10527 10529 10530 10533 10534 10535
## 2 1 3 2 4 1 2 1 2 1
## 10536 10538 10539 10542 10543 10544 10545 10546 10547 10548
## 1 1 2 3 1 4 1 1 3 2
## 10549 10551 10552 10553 10554 10555 10556 10557 10558 10559
## 1 2 2 1 1 3 1 2 5 1
## 10560 10561 10563 10564 10565 10566 10567 10568 10569 10571
## 1 1 1 3 2 1 3 1 1 1
## 10572 10573 10574 10576 10577 10579 10580 10582 10583 10584
## 2 2 3 1 2 2 2 2 3 1
## 10585 10586 10587 10590 10591 10593 10594 10595 10596 10597
## 2 2 3 4 2 1 2 1 1 1
## 10598 10601 10602 10603 10605 10606 10607 10608 10609 10611
## 2 4 1 4 1 1 1 1 2 1
## 10612 10615 10617 10618 10620 10621 10623 10624 10625 10626
## 1 3 1 1 1 1 2 1 3 1
## 10627 10629 10630 10631 10632 10634 10636 10638 10639 10640
## 1 2 1 1 1 1 3 1 2 1
## 10641 10642 10643 10644 10645 10648 10649 10651 10652 10653
## 1 3 2 1 1 1 2 1 2 2
## 10657 10658 10659 10660 10662 10665 10666 10667 10671 10673
## 2 1 1 1 3 1 4 1 2 3
## 10675 10676 10677 10678 10680 10682 10683 10684 10685 10686
## 1 1 2 4 1 1 2 2 2 2
## 10687 10689 10691 10693 10696 10697 10698 10699 10701 10703
## 6 1 2 1 1 1 3 1 1 3
## 10704 10707 10708 10709 10710 10711 10713 10714 10715 10716
## 1 4 3 3 2 3 1 3 2 4
## 10717 10719 10720 10721 10722 10724 10725 10726 10727 10729
## 1 2 1 2 3 2 2 3 1 1
## 10731 10732 10734 10735 10736 10738 10739 10740 10741 10742
## 1 2 2 3 2 2 2 1 2 1
## 10744 10747 10749 10750 10751 10754 10756 10761 10762 10763
## 1 1 1 1 2 2 4 3 3 3
## 10764 10766 10767 10768 10771 10773 10774 10775 10776 10777
## 2 1 2 4 1 2 4 4 2 2
## 10778 10779 10780 10782 10784 10786 10787 10788 10789 10790
## 1 1 4 2 2 1 2 1 1 1
## 10792 10793 10794 10795 10797 10798 10799 10800 10802 10804
## 1 3 1 4 1 1 4 1 1 1
## 10805 10806 10807 10808 10810 10811 10812 10813 10814 10816
## 2 3 1 1 1 1 1 1 1 4
## 10817 10818 10819 10820 10821 10822 10823 10825 10827 10828
## 2 2 1 1 5 2 2 4 1 1
## 10829 10830 10831 10833 10834 10836 10837 10838 10843 10844
## 1 1 2 1 3 1 1 1 2 1
## 10845 10846 10849 10850 10851 10852 10855 10856 10857 10858
## 4 1 2 1 1 1 4 1 1 3
## 10860 10861 10862 10863 10864 10867 10868 10870 10872 10875
## 1 2 1 1 1 2 1 1 2 5
## 10877 10878 10880 10883 10889 10891 10894 10896 10898 10900
## 1 2 2 1 1 1 1 2 1 1
## 10901 10903 10904 10905 10908 10909 10910 10911 10912 10913
## 2 2 1 2 1 1 1 1 1 1
## 10914 10915 10916 10918 10919 10920 10921 10923 10925 10926
## 1 2 2 1 1 1 2 2 1 1
## 10927 10928 10929 10930 10931 10933 10935 10936 10937 10938
## 1 2 4 3 1 2 2 1 2 2
## 10941 10942 10943 10945 10946 10947 10948 10949 10950 10951
## 1 1 2 1 4 2 2 1 2 1
## 10952 10954 10955 10958 10959 10960 10961 10963 10964 10965
## 1 1 1 1 5 2 2 1 1 1
## 10966 10967 10968 10970 10971 10972 10974 10975 10976 10979
## 1 2 1 2 1 1 2 1 1 2
## 10981 10985 10987 10988 10991 10992 10993 10995 10996 10997
## 2 1 1 1 1 1 2 1 1 2
## 10998 10999 11000 11001 11005 11009 11011 11013 11015 11016
## 1 2 1 2 1 1 2 1 1 1
## 11017 11020 11021 11023 11024 11025 11026 11027 11028 11030
## 1 2 1 2 3 3 1 1 2 1
## 11031 11032 11033 11034 11035 11036 11038 11041 11043 11046
## 1 2 1 2 1 4 2 1 1 2
## 11047 11049 11050 11051 11052 11053 11054 11056 11057 11058
## 2 2 5 1 2 2 1 1 3 1
## 11059 11060 11061 11063 11067 11068 11069 11070 11071 11072
## 1 3 3 1 4 4 1 1 4 1
## 11073 11077 11078 11079 11081 11082 11083 11084 11086 11087
## 1 2 1 1 2 3 1 2 1 3
## 11088 11091 11092 11095 11096 11097 11098 11099 11100 11102
## 1 1 5 1 2 1 2 5 1 1
## 11105 11106 11109 11110 11112 11116 11117 11118 11119 11121
## 1 2 1 2 2 2 2 1 3 1
## 11122 11123 11124 11126 11127 11128 11129 11130 11131 11132
## 1 1 3 1 3 2 1 1 1 3
## 11133 11134 11135 11136 11137 11139 11140 11141 11142 11143
## 2 3 3 3 1 2 2 1 2 2
## 11145 11146 11147 11150 11151 11154 11155 11156 11157 11159
## 2 2 2 2 3 1 1 4 2 1
## 11162 11166 11168 11169 11170 11171 11172 11173 11175 11177
## 4 3 3 1 1 3 3 3 1 2
## 11178 11180 11181 11182 11184 11185 11186 11187 11190 11191
## 1 2 3 1 1 2 1 4 3 5
## 11192 11194 11195 11198 11199 11200 11202 11203 11204 11205
## 2 1 2 2 1 1 1 2 3 1
## 11208 11209 11210 11211 11213 11214 11215 11216 11217 11220
## 3 1 2 1 2 4 2 3 2 2
## 11221 11224 11225 11226 11227 11228 11229 11231 11232 11233
## 1 3 2 4 1 1 1 2 1 4
## 11234 11235 11236 11238 11239 11240 11241 11243 11246 11247
## 3 2 1 2 2 3 3 1 3 2
## 11248 11250 11251 11252 11253 11257 11259 11260 11261 11265
## 1 1 1 2 2 4 2 1 1 1
## 11266 11267 11268 11269 11270 11271 11272 11273 11275 11277
## 1 1 2 4 6 3 2 2 1 2
## 11280 11282 11284 11291 11293 11294 11295 11296 11297 11298
## 1 3 1 1 1 5 2 1 1 2
## 11299 11300 11301 11302 11306 11307 11308 11309 11311 11312
## 1 2 2 1 2 2 2 3 2 5
## 11313 11314 11316 11320 11321 11322 11325 11327 11328 11330
## 2 1 2 1 1 1 3 1 2 3
## 11331 11333 11334 11335 11336 11337 11339 11341 11342 11344
## 2 1 1 2 1 3 5 2 4 1
## 11345 11346 11348 11349 11350 11351 11353 11357 11358 11359
## 2 1 1 1 3 1 2 1 2 3
## 11362 11363 11364 11365 11366 11367 11368 11369 11370 11371
## 1 1 1 1 1 2 3 3 1 2
## 11372 11373 11374 11375 11379 11380 11382 11383 11387 11388
## 1 3 3 1 2 1 3 1 1 2
## 11389 11391 11393 11394 11396 11398 11401 11402 11403 11404
## 1 3 2 1 1 1 1 2 4 2
## 11405 11406 11407 11408 11409 11410 11412 11413 11414 11416
## 3 1 1 3 2 1 1 1 3 1
## 11417 11418 11420 11422 11423 11425 11426 11427 11428 11429
## 3 1 1 5 1 3 2 2 2 2
## 11430 11433 11434 11435 11436 11437 11438 11439 11440 11441
## 1 2 4 2 1 1 1 3 2 2
## 11443 11444 11446 11448 11449 11450 11456 11457 11458 11459
## 2 2 1 1 3 1 1 1 3 3
## 11464 11466 11468 11469 11470 11473 11475 11479 11480 11481
## 3 4 1 2 1 1 1 2 3 3
## 11482 11483 11484 11490 11493 11494 11495 11497 11499 11500
## 2 1 1 1 2 3 4 1 1 2
## 11503 11506 11507 11508 11512 11515 11518 11519 11520 11522
## 2 1 3 1 4 2 3 2 2 1
## 11524 11525 11526 11527 11528 11530 11531 11532 11533 11534
## 1 2 3 1 2 3 1 1 2 4
## 11537 11541 11543 11544 11545 11546 11548 11551 11553 11555
## 2 2 2 1 1 1 1 3 1 4
## 11556 11558 11559 11561 11562 11563 11564 11565 11566 11567
## 2 2 2 3 1 1 2 2 1 1
## 11568 11569 11571 11572 11574 11575 11577 11578 11579 11582
## 1 1 2 1 2 1 1 2 2 1
## 11585 11587 11588 11589 11590 11591 11592 11594 11596 11597
## 4 2 2 2 2 2 1 1 1 1
## 11599 11600 11601 11602 11605 11606 11609 11610 11611 11613
## 3 3 4 5 1 1 1 3 1 1
## 11614 11616 11617 11618 11619 11621 11622 11623 11624 11625
## 2 1 1 2 2 2 1 2 1 2
## 11626 11628 11629 11630 11631 11632 11633 11636 11638 11639
## 1 2 2 1 2 3 1 1 1 2
## 11641 11642 11643 11644 11645 11646 11647 11649 11652 11653
## 1 1 3 2 2 1 1 1 1 3
## 11654 11655 11657 11658 11659 11660 11661 11662 11663 11664
## 1 1 1 1 1 1 2 3 1 1
## 11665 11666 11667 11669 11672 11673 11676 11677 11678 11679
## 3 3 1 2 2 1 1 1 1 4
## 11681 11683 11684 11686 11690 11691 11694 11695 11697 11699
## 1 1 1 1 1 2 1 2 1 2
## 11700 11701 11703 11704 11705 11706 11707 11709 11710 11711
## 4 3 4 2 2 1 1 3 3 1
## 11713 11715 11720 11722 11723 11724 11725 11726 11728 11729
## 2 1 3 1 1 2 3 2 1 1
## 11732 11733 11734 11735 11736 11737 11738 11739 11741 11743
## 1 1 1 1 1 1 3 1 1 2
## 11744 11745 11749 11751 11752 11753 11754 11755 11756 11757
## 3 1 2 4 1 1 1 1 1 1
## 11758 11761 11763 11765 11767 11769 11770 11772 11773 11775
## 1 3 1 2 2 2 1 1 1 1
## 11777 11778 11779 11780 11781 11782 11783 11787 11789 11790
## 2 1 2 2 2 2 2 1 1 1
## 11791 11792 11793 11794 11796 11797 11798 11799 11800 11801
## 1 2 1 1 2 2 1 3 1 1
## 11803 11806 11809 11810 11811 11812 11814 11815 11816 11817
## 1 1 4 1 1 1 1 2 1 2
## 11818 11819 11821 11822 11823 11825 11826 11828 11830 11832
## 1 1 1 1 2 4 5 1 1 4
## 11833 11834 11835 11836 11838 11839 11840 11843 11846 11847
## 2 1 2 1 2 1 1 1 2 2
## 11850 11851 11853 11854 11855 11856 11857 11858 11859 11860
## 1 1 1 3 2 1 1 1 1 2
## 11862 11864 11865 11866 11867 11868 11869 11871 11872 11875
## 2 1 1 2 5 1 1 1 1 1
## 11876 11877 11879 11880 11881 11882 11885 11887 11890 11893
## 1 2 1 2 1 2 3 1 3 1
## 11894 11895 11896 11897 11899 11900 11904 11905 11906 11908
## 1 1 1 1 4 1 1 1 4 1
## 11911 11912 11913 11914 11916 11917 11918 11919 11920 11922
## 1 1 1 2 3 1 2 2 1 2
## 11923 11925 11926 11927 11928 11929 11931 11932 11934 11936
## 1 2 1 3 3 1 1 2 1 2
## 11938 11945 11949 11950 11951 11953 11956 11959 11962 11964
## 1 3 2 2 2 2 1 2 3 1
## 11968 11972 11973 11975 11977 11979 11980 11981 11982 11983
## 2 1 1 1 2 2 1 2 1 2
## 11984 11985 11986 11988 11989 11993 11994 11995 11996 11997
## 1 2 1 1 1 2 1 1 1 2
## 11998 11999 12000 12001 12002 12003 12004 12005 12006 12007
## 2 4 1 1 3 2 1 2 3 1
## 12008 12009 12010 12011 12013 12014 12016 12017 12018 12019
## 1 1 1 1 3 1 1 1 2 1
## 12021 12022 12023 12026 12027 12029 12030 12031 12032 12033
## 1 1 1 2 2 1 2 2 1 2
## 12034 12035 12037 12039 12041 12042 12043 12045 12046 12047
## 1 1 2 1 3 6 1 1 2 2
## 12048 12051 12052 12054 12055 12057 12058 12060 12061 12062
## 1 2 2 2 1 1 2 2 1 1
## 12065 12067 12068 12069 12071 12072 12074 12075 12076 12079
## 2 3 1 1 1 2 1 1 1 3
## 12081 12083 12085 12087 12088 12089 12091 12092 12093 12094
## 1 2 2 2 2 3 1 1 2 2
## 12095 12098 12100 12101 12102 12103 12105 12107 12108 12109
## 1 1 2 3 1 3 3 1 2 1
## 12111 12112 12114 12116 12120 12122 12125 12126 12127 12129
## 1 1 4 2 2 1 2 1 1 3
## 12132 12135 12136 12137 12140 12141 12143 12146 12147 12148
## 2 1 1 3 6 2 3 2 1 2
## 12149 12150 12152 12153 12155 12157 12158 12159 12160 12163
## 1 1 1 1 1 5 1 3 1 1
## 12165 12167 12170 12172 12173 12174 12176 12177 12178 12180
## 1 1 1 1 1 1 2 1 2 1
## 12182 12183 12184 12185 12186 12187 12188 12189 12193 12194
## 2 1 2 2 2 1 1 3 1 1
## 12195 12196 12198 12200 12201 12202 12203 12205 12207 12209
## 1 1 3 1 2 2 1 3 2 2
## 12210 12211 12212 12213 12214 12217 12218 12220 12221 12222
## 1 2 3 1 4 1 1 3 1 3
## 12224 12227 12228 12229 12230 12233 12234 12235 12236 12237
## 2 1 1 2 1 2 1 2 1 1
## 12238 12239 12240 12241 12244 12245 12246 12247 12248 12249
## 1 2 1 1 1 1 1 2 2 1
## 12251 12254 12256 12257 12259 12261 12262 12263 12265 12267
## 2 1 2 2 1 1 1 4 1 2
## 12268 12270 12271 12273 12276 12277 12278 12279 12281 12283
## 1 1 1 1 1 1 2 2 2 1
## 12284 12285 12286 12287 12288 12289 12290 12291 12292 12293
## 1 1 3 1 3 1 2 3 1 1
## 12295 12297 12298 12299 12300 12303 12304 12305 12306 12308
## 2 1 1 3 1 1 1 1 2 1
## 12309 12310 12313 12315 12316 12317 12319 12323 12324 12327
## 1 1 1 2 2 1 1 1 2 1
## 12328 12329 12330 12331 12334 12335 12336 12338 12341 12343
## 2 1 2 1 3 1 1 1 3 3
## 12344 12345 12346 12347 12348 12349 12350 12351 12352 12355
## 1 1 1 3 2 2 1 1 1 2
## 12356 12358 12360 12361 12366 12371 12373 12374 12376 12377
## 1 2 1 3 1 1 2 1 1 1
## 12378 12379 12380 12382 12385 12387 12389 12390 12391 12392
## 2 2 2 1 1 2 1 2 1 1
## 12394 12395 12396 12397 12398 12402 12404 12406 12409 12412
## 1 1 2 1 1 3 5 1 1 1
## 12414 12416 12417 12420 12422 12423 12428 12429 12431 12432
## 1 2 1 4 2 1 1 2 1 2
## 12434 12435 12437 12438 12441 12442 12443 12444 12448 12449
## 1 1 1 1 2 1 1 4 1 2
## 12450 12451 12452 12453 12454 12456 12459 12460 12462 12463
## 1 1 1 1 1 2 2 1 3 2
## 12466 12471 12472 12475 12477 12478 12479 12482 12483 12487
## 1 2 1 1 2 2 1 3 2 1
## 12488 12489 12490 12491 12492 12493 12494 12495 12496 12497
## 2 1 1 1 1 1 1 1 2 3
## 12498 12499 12501 12502 12503 12504 12505 12507 12511 12512
## 1 1 2 1 2 3 1 2 2 2
## 12514 12515 12518 12519 12520 12521 12522 12523 12524 12525
## 1 2 1 1 1 1 1 2 1 1
## 12527 12529 12530 12531 12533 12534 12536 12543 12545 12547
## 2 3 3 3 1 3 2 1 2 3
## 12548 12549 12551 12552 12554 12558 12560 12562 12564 12565
## 4 3 1 4 2 2 2 1 3 1
## 12567 12568 12569 12571 12577 12578 12582 12584 12585 12587
## 3 1 1 2 1 1 1 2 2 3
## 12588 12589 12592 12593 12594 12596 12597 12598 12599 12601
## 1 3 1 2 1 3 2 1 2 1
## 12602 12604 12608 12609 12611 12612 12613 12614 12615 12617
## 1 1 1 2 2 2 3 1 2 3
## 12618 12619 12624 12625 12626 12629 12630 12632 12635 12636
## 3 1 1 1 1 1 2 1 2 1
## 12637 12638 12639 12640 12641 12644 12646 12647 12648 12649
## 2 1 1 1 3 3 2 1 4 1
## 12650 12651 12653 12654 12656 12657 12658 12660 12662 12667
## 1 1 1 2 2 1 1 1 2 5
## 12668 12669 12671 12672 12673 12674 12675 12676 12678 12680
## 1 1 1 2 2 3 2 1 1 1
## 12683 12687 12690 12691 12692 12694 12696 12697 12698 12701
## 1 1 1 1 1 1 2 2 1 1
## 12702 12704 12705 12706 12707 12708 12709 12710 12711 12712
## 1 1 3 2 2 1 1 2 1 1
## 12713 12714 12717 12720 12721 12722 12724 12725 12727 12728
## 2 1 1 2 1 1 1 3 1 2
## 12729 12730 12731 12733 12734 12735 12736 12737 12738 12740
## 1 3 1 1 1 2 1 3 2 2
## 12741 12742 12744 12746 12747 12749 12750 12751 12753 12756
## 1 2 1 2 2 2 1 1 1 1
## 12757 12761 12763 12764 12765 12766 12767 12768 12769 12770
## 2 1 2 2 2 2 1 3 5 3
## 12772 12774 12775 12778 12782 12787 12788 12789 12791 12793
## 1 3 1 1 3 1 3 4 1 1
## 12794 12797 12801 12802 12806 12807 12808 12812 12813 12816
## 3 2 2 1 1 1 1 2 2 3
## 12818 12819 12820 12824 12827 12828 12830 12831 12832 12836
## 3 2 1 3 1 1 1 3 1 3
## 12837 12838 12840 12841 12842 12843 12844 12845 12850 12851
## 2 2 2 1 2 1 1 4 1 2
## 12854 12855 12856 12858 12862 12863 12864 12865 12866 12867
## 1 2 1 1 1 3 1 2 1 1
## 12870 12871 12872 12873 12874 12876 12877 12878 12879 12880
## 3 2 3 1 2 3 1 1 3 1
## 12881 12882 12883 12885 12886 12890 12891 12897 12899 12900
## 1 1 1 1 1 3 1 1 1 1
## 12902 12903 12904 12906 12907 12908 12909 12910 12912 12914
## 2 2 1 2 1 1 1 1 1 1
## 12915 12916 12917 12918 12919 12923 12924 12925 12927 12928
## 1 1 1 2 1 1 3 2 1 2
## 12932 12933 12934 12935 12936 12937 12938 12944 12946 12948
## 1 3 1 1 2 1 1 1 1 1
## 12949 12951 12953 12956 12957 12958 12962 12964 12965 12966
## 1 1 1 2 1 1 2 1 3 2
## 12969 12971 12972 12976 12978 12979 12982 12983 12985 12987
## 2 1 2 2 3 1 2 3 1 1
## 12991 12992 12994 12997 13003 13004 13005 13006 13008 13010
## 3 1 2 3 1 2 2 1 1 3
## 13012 13013 13014 13015 13017 13018 13019 13022 13024 13025
## 1 1 1 1 1 2 1 1 1 1
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## 1 1 2 1 1 1 1 1 1 1
## 41375 41379 41388 41389 41416 41419 41421 41430 41468 41478
## 1 1 1 1 1 1 1 1 2 1
## 41479 41506 41511 41521 41562 41577 41591 41605 41607 41631
## 1 1 1 1 1 1 2 1 1 1
## 41634 41637 41640 41652 41657 41662 41695 41700 41703 41710
## 1 1 1 1 1 1 1 1 2 1
## 41732 41740 41765 41769 41777 41779 41780 41785 41793 41797
## 2 1 1 1 1 1 1 1 1 1
## 41800 41808 41835 41848 41864 41878 41913 41920 41933 41946
## 1 1 1 1 1 1 1 1 1 1
## 41957 41979 41987 41992 41997 42010 42014 42017 42032 42042
## 1 3 1 1 1 1 1 1 1 1
## 42062 42065 42095 42108 42109 42166 42171 42183 42193 42194
## 1 1 1 1 1 1 1 1 1 1
## 42205 42252 42271 42286 42308 42316 42330 42335 42342 42347
## 1 1 1 1 1 1 1 3 1 1
## 42359 42362 42368 42395 42397 42403 42463 42505 42506 42513
## 1 1 1 1 1 1 1 1 1 1
## 42518 42543 42553 42556 42594 42612 42616 42626 42639 42640
## 1 1 1 1 1 1 1 1 1 1
## 42641 42647 42672 42674 42682 42705 42707 42732 42734 42742
## 1 1 1 1 1 1 1 1 2 1
## 42745 42747 42749 42784 42812 42832 42849 42852 42878 42881
## 1 1 1 1 1 1 1 1 1 1
## 42888 42894 42909 42922 42932 42966 42974 42993 42996 43037
## 1 1 1 1 1 1 1 1 1 1
## 43047 43053 43057 43076 43090 43114 43115 43146 43153 43155
## 1 1 1 1 1 1 1 1 1 1
## 43176 43187 43225 43226 43240 43252 43259 43278 43301 43306
## 1 1 1 1 1 2 1 2 1 1
## 43308 43320 43321 43387 43407 43417 43430 43431 43444 43467
## 2 1 1 2 1 1 1 1 1 1
## 43476 43479 43522 43524 43536 43542 43547 43548 43549 43552
## 1 1 1 1 1 1 1 1 1 1
## 43560 43566 43621 43633 43662 43678 43681 43683 43695 43711
## 1 1 1 1 1 1 1 1 1 1
## 43718 43734 43762 43803 43846 43885 43890 43900 43902 43945
## 1 1 1 1 1 1 1 1 1 1
## 43962 43964 44026 44027 44046 44059 44074 44105 44110 44119
## 1 1 1 1 1 1 1 1 1 1
## 44144 44175 44185 44222 44233 44274 44277 44307 44312 44353
## 1 1 1 1 1 1 1 1 1 1
## 44375 44396 44401 44402 44457 44514 44518 44529 44534 44562
## 1 1 1 1 1 1 1 1 1 1
## 44564 44577 44593 44595 44598 44608 44613 44647 44654 44659
## 1 1 1 1 1 1 1 1 1 1
## 44692 44717 44723 44728 44748 44756 44776 44811 44815 44842
## 1 1 1 1 1 1 1 1 1 2
## 44854 44865 44866 44890 44895 44897 44899 44902 44925 44926
## 1 1 1 1 1 1 1 1 1 1
## 44929 44935 44962 44966 44976 44995 45000 45037 45060 45127
## 1 1 1 1 1 1 1 1 1 1
## 45142 45159 45230 45243 45294 45302 45313 45320 45327 45353
## 1 1 1 1 1 1 1 1 1 2
## 45389 45391 45392 45434 45480 45498 45510 45517 45556 45599
## 1 1 1 1 1 1 1 1 1 1
## 45608 45625 45637 45643 45657 45660 45689 45742 45767 45776
## 1 1 1 1 1 2 1 1 1 1
## 45782 45784 45790 45795 45801 45824 45862 45891 45909 45913
## 1 1 1 1 1 1 1 1 1 1
## 45955 45998 46008 46028 46080 46147 46159 46168 46177 46195
## 1 1 1 1 1 1 1 1 1 1
## 46204 46224 46265 46307 46310 46315 46322 46338 46367 46392
## 1 1 1 2 1 1 1 1 3 1
## 46412 46416 46432 46444 46449 46480 46483 46485 46492 46509
## 1 1 1 1 1 1 1 1 1 1
## 46541 46553 46557 46558 46563 46564 46574 46581 46609 46621
## 1 1 1 1 1 1 2 1 1 1
## 46629 46630 46643 46659 46682 46683 46709 46729 46733 46735
## 1 1 1 1 1 1 1 1 1 1
## 46742 46748 46751 46761 46778 46786 46789 46794 46814 46823
## 1 1 1 1 1 1 1 1 1 1
## 46853 46859 46863 46865 46866 46870 46876 46892 46893 46948
## 2 1 1 1 1 1 1 1 1 1
## 46998 47045 47057 47077 47094 47144 47185 47197 47211 47232
## 1 1 1 1 1 1 1 1 2 1
## 47242 47286 47329 47343 47367 47375 47378 47392 47427 47428
## 1 1 1 1 1 1 1 1 1 1
## 47436 47439 47451 47472 47477 47521 47546 47553 47557 47582
## 2 1 1 1 1 1 1 2 1 1
## 47586 47599 47615 47647 47667 47675 47702 47720 47745 47764
## 1 1 1 1 1 1 1 2 1 2
## 47765 47775 47797 47801 47807 47844 47854 47875 47883 47912
## 1 1 1 2 1 1 1 1 1 1
## 47940 47949 48036 48043 48048 48068 48071 48082 48099 48127
## 1 1 1 1 1 1 1 1 1 1
## 48136 48153 48170 48171 48174 48177 48199 48202 48215 48235
## 1 1 1 1 1 1 1 1 1 1
## 48247 48253 48262 48277 48290 48294 48304 48360 48397 48437
## 1 2 1 1 1 1 1 1 1 1
## 48510 48520 48521 48575 48579 48614 48645 48660 48691 48745
## 1 1 1 1 1 1 1 2 1 1
## 48773 48774 48795 48858 48897 48929 48976 48993 49011 49021
## 1 2 1 1 1 1 1 1 1 1
## 49024 49026 49080 49090 49132 49167 49214 49223 49225 49238
## 1 1 1 1 1 1 1 1 1 1
## 49240 49252 49264 49282 49286 49319 49359 49375 49379 49415
## 1 1 1 1 1 1 2 1 1 1
## 49416 49422 49462 49483 49518 49535 49582 49600 49620 49638
## 1 1 1 1 1 1 1 1 1 1
## 49722 49770 49778 49787 49810 49832 49841 49852 49856 49878
## 1 1 1 1 1 1 1 1 1 1
## 49906 49916 49930 49933 49944 49997 50012 50029 50070 50103
## 1 1 1 1 1 1 1 1 1 1
## 50119 50199 50202 50230 50231 50271 50303 50326 50358 50364
## 1 1 1 1 1 1 1 1 1 1
## 50409 50410 50425 50429 50440 50441 50449 50458 50467 50523
## 1 1 1 2 1 1 1 1 1 1
## 50524 50530 50571 50603 50632 50651 50681 50682 50717 50725
## 1 1 1 1 1 2 1 1 1 1
## 50729 50741 50751 50769 50780 50788 50792 50800 50804 50857
## 1 1 1 1 1 1 1 1 1 1
## 50880 50882 50911 50913 50938 50950 50994 51017 51047 51057
## 1 1 1 1 1 1 1 1 2 1
## 51131 51132 51194 51201 51206 51222 51223 51224 51233 51262
## 1 1 1 1 1 1 1 1 1 1
## 51278 51328 51336 51359 51425 51467 51477 51525 51616 51623
## 1 1 1 1 1 1 1 1 2 1
## 51636 51700 51744 51756 51784 51785 51801 51842 51884 51906
## 1 1 1 1 1 1 2 2 1 1
## 51918 51939 51970 51977 51981 52027 52029 52088 52090 52092
## 1 1 1 1 1 1 1 1 1 2
## 52132 52151 52185 52197 52198 52212 52213 52253 52274 52296
## 2 1 1 1 1 1 1 1 1 1
## 52303 52326 52359 52379 52393 52414 52426 52429 52491 52493
## 1 1 1 1 1 1 1 1 1 1
## 52554 52593 52596 52603 52612 52636 52638 52715 52719 52738
## 1 1 1 1 1 1 1 1 1 1
## 52752 52799 52808 52851 52879 52882 52897 52934 52938 52940
## 1 1 1 1 1 1 1 1 1 1
## 52953 53002 53009 53045 53130 53133 53140 53142 53147 53161
## 1 1 1 1 1 1 2 1 1 2
## 53182 53187 53290 53298 53351 53356 53383 53460 53485 53498
## 1 1 1 1 1 1 1 1 1 1
## 53620 53637 53644 53647 53701 53704 53731 53750 53762 53769
## 1 1 1 1 1 1 1 1 1 1
## 53773 53784 53786 53844 53851 53913 53924 53943 53956 54059
## 1 1 1 1 1 1 1 1 1 1
## 54070 54087 54088 54129 54142 54156 54162 54225 54254 54261
## 1 1 1 1 1 1 1 1 1 1
## 54278 54382 54385 54471 54475 54481 54513 54539 54604 54715
## 1 1 1 1 1 1 1 1 1 1
## 54726 54756 54790 54808 54825 54857 54871 54898 54933 54936
## 1 1 1 1 1 2 1 1 1 1
## 54984 55067 55092 55124 55126 55202 55215 55234 55249 55300
## 1 1 1 1 1 1 1 1 1 1
## 55341 55348 55381 55389 55419 55446 55463 55477 55483 55615
## 1 1 1 1 1 1 1 1 1 1
## 55621 55625 55642 55659 55710 55745 55796 55797 55845 55864
## 1 1 1 1 1 1 1 1 1 1
## 55865 55881 55893 55910 55967 55982 56052 56098 56115 56121
## 1 2 1 1 1 1 1 2 1 1
## 56132 56208 56323 56343 56395 56399 56422 56468 56498 56532
## 1 1 1 1 1 1 1 1 1 1
## 56552 56573 56628 56629 56644 56652 56730 56732 56786 56843
## 1 1 1 1 1 1 1 1 1 1
## 56876 56900 56905 56933 57002 57005 57020 57137 57166 57173
## 1 1 1 1 1 1 1 1 1 1
## 57201 57209 57229 57249 57254 57335 57340 57397 57470 57534
## 1 1 1 1 1 1 1 1 1 1
## 57541 57576 57599 57649 57711 57765 57821 57824 57893 57899
## 1 1 1 1 1 1 1 1 1 1
## 57922 57945 58054 58083 58088 58119 58143 58172 58283 58304
## 1 1 1 1 1 1 1 1 1 2
## 58351 58368 58372 58440 58443 58474 58499 58506 58568 58583
## 1 1 2 1 1 1 1 1 1 1
## 58617 58645 58655 58728 58758 58778 58855 58865 59000 59095
## 1 1 1 1 1 1 1 1 1 1
## 59120 59125 59169 59177 59185 59241 59299 59341 59349 59418
## 1 1 1 1 1 1 1 1 1 1
## 59429 59466 59475 59486 59526 59528 59532 59572 59607 59660
## 1 1 1 1 1 1 1 1 1 1
## 59792 59796 59879 59883 59915 60002 60028 60029 60042 60116
## 2 1 1 1 1 1 1 1 1 1
## 60213 60215 60251 60384 60393 60450 60451 60468 60520 60631
## 1 1 1 1 1 1 1 1 1 1
## 60687 60732 60843 60875 60878 60962 60968 60980 60987 60988
## 1 1 1 1 1 1 1 1 1 1
## 61075 61107 61132 61142 61197 61241 61243 61263 61337 61399
## 1 1 1 2 1 1 1 1 1 1
## 61401 61425 61476 61528 61587 61648 61682 61716 61780 61782
## 1 1 1 1 1 1 1 1 1 1
## 61845 61894 61939 62009 62070 62077 62100 62152 62161 62212
## 2 1 2 1 1 1 1 1 1 1
## 62232 62258 62292 62389 62433 62450 62453 62468 62482 62499
## 1 1 2 1 1 1 1 1 1 1
## 62546 62606 62687 62723 62744 62750 62767 62864 62913 62919
## 1 1 1 1 1 1 1 1 1 1
## 63004 63061 63070 63094 63118 63159 63173 63265 63328 63433
## 1 1 1 1 1 1 1 1 1 1
## 63448 63449 63505 63575 63635 63666 63735 63800 63811 63834
## 1 1 1 1 1 1 1 1 1 1
## 63913 64149 64150 64168 64173 64192 64306 64408 64435 64438
## 1 1 1 1 1 1 1 1 1 1
## 64446 64491 64506 64546 64577 64611 64733 64753 64767 64815
## 1 1 1 1 1 1 1 1 1 1
## 64824 64840 64904 65096 65286 65310 65371 65412 65656 65767
## 1 1 1 1 1 1 1 1 1 1
## 65808 65827 65863 65871 65977 65998 66121 66379 66406 66514
## 1 1 1 1 1 1 1 1 1 1
## 66598 66665 66685 66740 66767 66861 66890 66978 66997 67023
## 1 1 1 1 1 1 1 2 1 1
## 67075 67083 67144 67261 67262 67287 67360 67507 67635 67685
## 1 1 1 1 1 1 1 1 1 1
## 67693 67719 67720 67866 67873 67876 67939 67956 68119 68170
## 1 1 1 1 1 1 1 1 1 1
## 68235 68372 68594 68614 68648 68720 68781 68791 68879 68886
## 1 1 1 2 1 1 1 3 1 1
## 68910 68923 68946 68968 68985 69100 69187 69233 69292 69364
## 1 2 1 1 1 1 1 1 1 1
## 69530 69597 69732 69798 69824 69845 69991 70006 70012 70053
## 1 1 1 1 1 1 1 1 1 1
## 70060 70102 70135 70142 70187 70518 70543 70603 70627 70694
## 1 1 1 1 1 1 1 1 1 1
## 70729 70828 70846 70978 71015 71017 71037 71156 71200 71281
## 1 1 1 1 1 1 1 1 1 1
## 71296 71299 71323 71516 71635 71648 71886 72050 72095 72195
## 1 1 1 1 1 1 1 1 1 1
## 72204 72290 72299 72445 72527 72530 72575 72590 72673 72683
## 1 1 1 1 1 1 1 1 1 1
## 72700 72855 72898 72949 73032 73042 73106 73114 73201 73212
## 1 1 1 1 1 1 1 1 1 1
## 73312 73341 73437 73442 73593 73627 73691 73941 74000 74075
## 1 2 1 1 1 1 1 1 1 1
## 74112 74346 74434 74565 74729 74841 74905 74952 74984 75004
## 1 1 1 1 1 1 1 1 1 1
## 75048 75095 75186 75194 75271 75295 75337 75514 75603 75645
## 2 1 1 2 1 1 1 1 1 2
## 75664 75723 75792 75998 76009 76105 76203 76283 76295 76307
## 1 1 1 1 1 1 1 1 1 1
## 76340 76368 76531 76535 76590 76643 76647 76763 76901 77000
## 1 1 1 1 1 1 1 1 1 1
## 77112 77442 77520 77545 77582 77601 77610 77622 77742 77932
## 1 1 1 1 1 1 1 1 1 1
## 78252 78367 78697 78712 78968 79129 79264 79389 79456 79457
## 1 1 1 1 1 1 1 1 1 1
## 79552 79668 79705 79750 79834 79886 79980 79991 80071 80188
## 1 1 1 1 1 1 1 1 1 2
## 80204 80284 80398 80463 80501 80863 80943 81132 81417 81655
## 1 1 1 2 1 1 1 1 1 1
## 81716 81786 81925 82064 82273 82899 83253 83353 83418 83506
## 2 1 1 1 1 1 1 1 1 1
## 83585 83610 83685 83854 83997 84051 84190 84348 84362 84393
## 1 1 1 1 1 1 1 1 1 1
## 84528 84807 85170 85422 85576 85684 85966 85982 86087 86255
## 1 1 1 1 1 1 1 1 1 1
## 86321 86360 86606 86609 86627 86635 86986 87240 87502 87544
## 1 1 1 1 1 1 1 1 1 1
## 87584 87696 87744 87869 88098 88373 88463 88557 88731 88853
## 1 1 1 1 1 1 1 1 1 1
## 89098 89395 89435 89565 89889 89968 89975 90163 90432 90465
## 2 1 1 1 1 1 1 1 1 1
## 90521 91190 91469 91491 91495 91713 91851 92039 92165 92191
## 1 1 1 1 1 1 1 1 1 1
## 92432 92550 92669 92679 92931 92966 93229 93528 93771 93804
## 1 1 1 1 1 1 1 1 1 1
## 93967 94078 94151 94373 94685 95162 95170 95217 96523 96802
## 1 1 1 1 1 1 1 1 1 1
## 96936 97116 97711 97819 97837 98581 98642 98655 98808 99234
## 1 2 1 1 1 1 1 1 1 1
## 99282 99852 100064 100242 100331 100418 100720 101137 101378 101379
## 1 1 1 1 1 1 1 1 1 1
## 102032 102622 102923 103429 103439 103867 104230 104251 104382 104458
## 1 1 1 1 1 1 1 1 1 1
## 106363 106438 106522 107113 107198 107561 108536 108562 108597 109240
## 1 2 1 1 1 1 1 1 1 1
## 109295 109919 110192 110287 110298 111330 112127 112300 112728 113675
## 1 1 1 1 1 1 1 1 1 1
## 113760 114036 115178 115732 115933 116522 116803 117084 117187 117335
## 1 1 1 1 1 1 1 1 1 1
## 117446 119095 121752 122775 123426 125139 127745 128681 129316 130504
## 1 1 1 1 1 1 1 1 1 1
## 132196 135434 135779 135971 136144 137231 137952 138443 138698 138770
## 1 1 1 1 1 3 1 1 1 1
## 139658 139983 144834 145776 146065 146764 147213 148358 148896 150093
## 1 1 2 1 1 1 1 1 1 1
## 150156 150601 152748 153140 153740 155214 156486 159040 162460 173890
## 1 1 1 1 1 1 1 1 1 1
## 178875 179610 183593 184948 188295 193613 214394 215403 221609 223881
## 1 2 1 1 1 1 1 1 2 1
## 230403 234480 254751 282391 478615 572688 788263
## 1 1 1 1 1 1 1here we observe that 29% are 0
finding the less than value 6000 & 9000
NROW(data[which(data$x233<=6000),])## [1] 78835here we observed 78% less than 6000
NROW(data[which(data$x233<=15000),])## [1] 91555here we observed 91% are less than 15000
Now visualize the statistics
##Histogram
hist(data$x233,
main = "Histogram of x233",
xlab = "x233")
abline(v =mean(data$x233, na.rm = TRUE),col = "red",
lwd = 2)
Boxplot
boxplot(data$x233,
main = toupper("Boxplot of x233"),
ylab = "x233",
col = "red")
## we got the outliers ##Density Plot
d <- density(data$x233, na.rm = TRUE)
plot(d, main = "Kernel density of x233")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x233, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
here 0 has more density there is no missing values and the bandwidth is 320.4,the skewness is a postive because the tail will have the large values,this variable has descrete values these are like transcation.
summary of the variable x234
summary(data$x234)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 375 1092 3137 3680 781251 19110observe that values from 0 to 781251, median is 1092 & mean is 3137
Description of the variable x234
describe(data$x234)## data$x234
## n missing distinct Info Mean Gmd .05 .10
## 80890 19110 13800 0.998 3137 4265 0 0
## .25 .50 .75 .90 .95
## 375 1092 3680 8289 12790
##
## 0 (65944, 0.815), 10000 (11966, 0.148), 20000 (2173, 0.027), 30000 (517,
## 0.006), 40000 (145, 0.002), 50000 (73, 0.001), 60000 (34, 0.000), 70000
## (11, 0.000), 80000 (3, 0.000), 90000 (4, 0.000), 1e+05 (5, 0.000), 110000
## (3, 0.000), 120000 (1, 0.000), 130000 (1, 0.000), 140000 (4, 0.000),
## 160000 (1, 0.000), 190000 (2, 0.000), 290000 (1, 0.000), 380000 (1,
## 0.000), 780000 (1, 0.000)Here the kurtosis is positive and its has a large data in tails .
Total number of values excluding NA
NROW(data$x234) - NROW(data[which(is.na(data$x234)),])## [1] 80890Here the value is 80890 because there is 20% of missing values.
table(data$x234)##
## 0 1 2 3 4 5 6 7 8 9
## 10239 18 24 18 17 19 23 12 26 37
## 10 11 12 13 14 15 16 17 18 19
## 25 23 22 14 24 23 22 19 16 23
## 20 21 22 23 24 25 26 27 28 29
## 30 13 12 14 20 38 18 31 19 32
## 30 31 32 33 34 35 36 37 38 39
## 24 16 23 31 19 24 14 15 22 27
## 40 41 42 43 44 45 46 47 48 49
## 34 17 22 20 19 29 25 22 21 29
## 50 51 52 53 54 55 56 57 58 59
## 32 23 20 22 30 26 24 15 24 29
## 60 61 62 63 64 65 66 67 68 69
## 20 16 21 18 22 26 20 25 14 17
## 70 71 72 73 74 75 76 77 78 79
## 19 9 20 30 29 56 20 20 23 21
## 80 81 82 83 84 85 86 87 88 89
## 20 27 21 21 17 26 39 36 19 23
## 90 91 92 93 94 95 96 97 98 99
## 24 21 20 20 21 29 28 29 21 19
## 100 101 102 103 104 105 106 107 108 109
## 46 31 24 19 21 33 25 30 32 32
## 110 111 112 113 114 115 116 117 118 119
## 16 23 19 26 27 13 29 14 28 18
## 120 121 122 123 124 125 126 127 128 129
## 26 33 21 28 15 26 32 17 13 27
## 130 131 132 133 134 135 136 137 138 139
## 25 12 26 25 35 26 26 18 24 27
## 140 141 142 143 144 145 146 147 148 149
## 22 20 26 23 23 25 25 21 17 26
## 150 151 152 153 154 155 156 157 158 159
## 30 26 32 19 16 37 21 27 18 18
## 160 161 162 163 164 165 166 167 168 169
## 36 22 27 23 21 15 16 22 20 30
## 170 171 172 173 174 175 176 177 178 179
## 22 34 23 30 23 31 22 11 27 16
## 180 181 182 183 184 185 186 187 188 189
## 19 28 24 20 21 23 22 20 26 23
## 190 191 192 193 194 195 196 197 198 199
## 21 28 22 22 24 32 27 25 22 29
## 200 201 202 203 204 205 206 207 208 209
## 51 22 22 25 29 22 25 23 25 28
## 210 211 212 213 214 215 216 217 218 219
## 19 22 29 21 27 17 25 11 30 27
## 220 221 222 223 224 225 226 227 228 229
## 26 18 29 28 27 31 28 18 23 21
## 230 231 232 233 234 235 236 237 238 239
## 18 31 20 31 21 23 25 23 20 27
## 240 241 242 243 244 245 246 247 248 249
## 29 27 26 26 21 25 32 41 22 34
## 250 251 252 253 254 255 256 257 258 259
## 36 20 25 43 27 39 21 23 30 29
## 260 261 262 263 264 265 266 267 268 269
## 27 28 20 31 30 31 28 35 31 25
## 270 271 272 273 274 275 276 277 278 279
## 22 31 31 26 31 33 29 38 36 41
## 280 281 282 283 284 285 286 287 288 289
## 36 30 30 37 21 46 27 30 40 28
## 290 291 292 293 294 295 296 297 298 299
## 22 31 43 33 36 38 41 39 55 44
## 300 301 302 303 304 305 306 307 308 309
## 61 46 40 47 43 41 36 29 41 34
## 310 311 312 313 314 315 316 317 318 319
## 26 35 21 35 27 28 32 31 42 33
## 320 321 322 323 324 325 326 327 328 329
## 33 17 26 34 30 31 22 26 31 37
## 330 331 332 333 334 335 336 337 338 339
## 43 36 42 35 36 39 34 34 27 51
## 340 341 342 343 344 345 346 347 348 349
## 25 25 32 31 25 31 21 34 27 27
## 350 351 352 353 354 355 356 357 358 359
## 33 25 27 27 30 25 27 40 30 31
## 360 361 362 363 364 365 366 367 368 369
## 35 27 23 30 32 31 35 32 22 32
## 370 371 372 373 374 375 376 377 378 379
## 33 30 49 28 30 34 41 28 36 27
## 380 381 382 383 384 385 386 387 388 389
## 49 47 41 46 48 36 38 33 41 33
## 390 391 392 393 394 395 396 397 398 399
## 34 40 42 61 67 44 45 42 37 47
## 400 401 402 403 404 405 406 407 408 409
## 60 60 35 32 34 43 46 46 37 41
## 410 411 412 413 414 415 416 417 418 419
## 54 47 41 36 41 49 52 52 51 51
## 420 421 422 423 424 425 426 427 428 429
## 47 62 70 79 101 98 74 44 74 76
## 430 431 432 433 434 435 436 437 438 439
## 80 76 98 81 74 70 67 80 81 59
## 440 441 442 443 444 445 446 447 448 449
## 64 62 76 73 59 62 65 65 54 72
## 450 451 452 453 454 455 456 457 458 459
## 79 70 55 56 50 57 53 55 54 52
## 460 461 462 463 464 465 466 467 468 469
## 48 37 44 47 48 46 42 55 49 52
## 470 471 472 473 474 475 476 477 478 479
## 38 38 37 47 41 36 53 37 43 49
## 480 481 482 483 484 485 486 487 488 489
## 37 27 42 53 36 45 50 40 37 24
## 490 491 492 493 494 495 496 497 498 499
## 42 41 57 42 47 41 28 39 46 54
## 500 501 502 503 504 505 506 507 508 509
## 83 34 56 53 47 46 48 51 42 47
## 510 511 512 513 514 515 516 517 518 519
## 50 40 32 32 41 37 42 35 33 31
## 520 521 522 523 524 525 526 527 528 529
## 41 29 37 37 27 34 19 32 47 27
## 530 531 532 533 534 535 536 537 538 539
## 38 40 33 36 43 44 34 38 40 37
## 540 541 542 543 544 545 546 547 548 549
## 44 46 49 42 46 33 45 42 43 39
## 550 551 552 553 554 555 556 557 558 559
## 36 34 43 40 29 46 37 34 35 30
## 560 561 562 563 564 565 566 567 568 569
## 41 32 31 36 23 26 37 38 21 33
## 570 571 572 573 574 575 576 577 578 579
## 22 26 26 34 21 31 35 29 34 25
## 580 581 582 583 584 585 586 587 588 589
## 36 36 33 30 40 23 45 37 28 39
## 590 591 592 593 594 595 596 597 598 599
## 29 34 31 43 40 29 33 39 36 33
## 600 601 602 603 604 605 606 607 608 609
## 26 39 19 33 34 26 32 37 24 33
## 610 611 612 613 614 615 616 617 618 619
## 17 24 20 21 31 23 21 27 31 25
## 620 621 622 623 624 625 626 627 628 629
## 37 23 18 26 20 27 24 20 26 28
## 630 631 632 633 634 635 636 637 638 639
## 26 20 25 22 29 24 23 21 31 19
## 640 641 642 643 644 645 646 647 648 649
## 20 28 28 21 26 39 25 26 19 17
## 650 651 652 653 654 655 656 657 658 659
## 25 22 24 28 28 17 22 20 13 20
## 660 661 662 663 664 665 666 667 668 669
## 13 42 21 30 24 22 31 16 22 20
## 670 671 672 673 674 675 676 677 678 679
## 18 20 31 11 31 31 31 24 23 26
## 680 681 682 683 684 685 686 687 688 689
## 26 27 31 25 24 25 32 22 20 27
## 690 691 692 693 694 695 696 697 698 699
## 29 27 24 28 16 19 32 27 27 31
## 700 701 702 703 704 705 706 707 708 709
## 28 25 27 27 21 26 21 29 25 23
## 710 711 712 713 714 715 716 717 718 719
## 29 36 28 31 17 32 23 21 23 21
## 720 721 722 723 724 725 726 727 728 729
## 16 28 25 15 26 31 21 31 13 15
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## 18433 18439 18448 18452 18454 18474 18483 18494 18496 18499
## 1 1 1 1 1 1 1 1 1 1
## 18500 18504 18512 18513 18516 18519 18521 18524 18530 18531
## 1 2 1 1 1 1 1 1 1 1
## 18551 18556 18557 18571 18587 18590 18591 18602 18613 18614
## 1 2 1 1 1 1 1 1 1 1
## 18615 18617 18621 18642 18656 18667 18672 18678 18681 18682
## 1 1 1 2 1 1 2 2 1 1
## 18684 18685 18697 18699 18707 18711 18712 18716 18724 18728
## 1 1 1 1 1 1 1 1 1 1
## 18731 18733 18739 18742 18743 18745 18746 18747 18748 18753
## 1 1 1 1 1 1 1 1 1 2
## 18758 18762 18763 18764 18766 18767 18768 18769 18771 18776
## 1 1 1 1 1 2 1 1 1 1
## 18777 18792 18795 18796 18800 18801 18805 18808 18810 18820
## 1 1 1 2 1 1 2 1 1 1
## 18824 18825 18827 18829 18839 18842 18848 18850 18854 18855
## 1 1 1 1 1 1 1 1 1 1
## 18859 18872 18875 18881 18883 18890 18900 18901 18903 18905
## 1 2 1 1 1 1 2 1 1 1
## 18912 18917 18923 18924 18925 18934 18944 18947 18958 18959
## 1 1 1 1 1 1 1 1 1 1
## 18966 18970 18973 18982 18993 18994 19000 19014 19020 19025
## 1 1 1 1 1 1 2 2 1 1
## 19038 19040 19045 19050 19052 19058 19061 19062 19063 19065
## 1 1 1 1 1 1 1 1 1 1
## 19068 19071 19073 19078 19081 19086 19090 19092 19093 19094
## 1 1 1 1 1 1 1 1 1 2
## 19101 19102 19104 19111 19115 19126 19129 19131 19135 19147
## 1 1 1 2 2 1 1 1 1 1
## 19157 19161 19172 19173 19182 19185 19188 19195 19196 19200
## 1 2 1 1 1 1 1 1 1 2
## 19206 19209 19213 19214 19216 19220 19221 19224 19238 19246
## 1 1 1 2 1 1 1 1 1 1
## 19257 19258 19259 19267 19276 19279 19281 19285 19286 19287
## 1 1 1 1 1 1 1 1 1 1
## 19288 19298 19318 19323 19328 19331 19333 19339 19340 19349
## 1 1 1 1 1 1 2 1 1 1
## 19352 19356 19359 19378 19395 19404 19418 19424 19435 19436
## 1 1 3 1 1 1 1 1 1 1
## 19441 19448 19453 19455 19465 19474 19475 19479 19485 19499
## 1 1 2 1 1 1 1 1 1 1
## 19505 19507 19526 19528 19539 19543 19546 19547 19551 19555
## 1 1 1 1 1 1 2 1 1 1
## 19557 19566 19582 19587 19589 19592 19596 19598 19600 19601
## 1 1 1 1 3 1 1 1 1 1
## 19607 19608 19609 19611 19621 19624 19641 19643 19657 19658
## 1 1 1 1 1 1 1 1 1 1
## 19661 19666 19670 19671 19672 19676 19681 19689 19696 19697
## 1 2 1 1 1 1 1 1 1 1
## 19699 19707 19710 19712 19717 19718 19722 19724 19735 19737
## 1 1 1 1 1 1 1 1 1 1
## 19738 19739 19740 19742 19743 19747 19754 19755 19768 19783
## 1 1 2 1 1 2 1 1 1 1
## 19785 19786 19792 19797 19801 19805 19808 19811 19818 19834
## 1 1 1 1 1 1 1 2 1 1
## 19835 19842 19843 19853 19854 19858 19860 19870 19874 19877
## 1 1 1 1 1 1 1 1 1 1
## 19884 19885 19888 19893 19894 19896 19902 19905 19917 19921
## 1 2 2 2 3 1 1 1 1 2
## 19925 19932 19933 19940 19944 19951 19954 19956 19964 19973
## 1 1 1 1 1 1 1 1 1 1
## 19975 19976 19980 19981 19982 19984 19993 19995 20008 20009
## 1 2 1 1 1 1 1 2 2 1
## 20010 20019 20022 20026 20035 20040 20042 20044 20047 20050
## 1 1 1 1 1 2 1 1 1 1
## 20051 20053 20065 20068 20077 20079 20080 20085 20087 20093
## 2 2 1 1 1 1 1 1 1 1
## 20111 20112 20114 20120 20129 20130 20140 20143 20160 20164
## 1 4 1 1 1 1 1 1 1 1
## 20165 20166 20171 20182 20186 20192 20193 20198 20204 20209
## 1 1 1 1 1 1 1 1 1 1
## 20214 20218 20238 20247 20252 20265 20266 20267 20272 20274
## 1 2 1 1 3 1 1 1 2 1
## 20281 20283 20287 20302 20326 20342 20345 20359 20374 20390
## 1 1 1 1 1 1 1 1 1 2
## 20403 20407 20408 20409 20417 20420 20437 20446 20447 20451
## 1 1 1 1 2 1 1 1 1 2
## 20456 20463 20483 20486 20499 20500 20502 20514 20520 20523
## 1 1 2 1 1 1 1 1 1 1
## 20529 20530 20536 20537 20546 20554 20559 20563 20564 20570
## 1 1 1 1 1 2 1 1 1 1
## 20577 20579 20583 20584 20588 20594 20608 20609 20616 20619
## 1 1 1 1 1 2 1 2 1 1
## 20621 20633 20636 20637 20663 20664 20666 20686 20689 20693
## 1 1 1 1 1 2 1 1 2 1
## 20702 20704 20707 20735 20745 20749 20750 20757 20759 20764
## 1 1 1 1 1 2 1 1 1 1
## 20770 20773 20775 20779 20784 20793 20800 20809 20810 20831
## 1 1 1 1 1 1 1 1 1 1
## 20832 20836 20840 20841 20853 20864 20865 20873 20886 20897
## 1 1 1 1 1 1 2 1 1 1
## 20900 20909 20914 20924 20926 20931 20956 20964 20965 20967
## 1 1 1 1 1 1 1 1 1 1
## 20971 20972 20975 20978 20981 20984 20986 20990 21000 21007
## 1 1 1 1 1 1 1 1 1 1
## 21027 21037 21038 21039 21040 21043 21048 21051 21054 21055
## 1 1 1 1 1 2 1 1 1 2
## 21064 21069 21074 21082 21090 21100 21105 21111 21113 21114
## 1 2 1 1 1 1 1 2 1 1
## 21115 21119 21121 21125 21126 21133 21139 21141 21149 21150
## 1 1 1 1 1 1 1 1 1 1
## 21162 21168 21173 21183 21186 21187 21192 21194 21197 21200
## 1 1 1 1 1 1 1 1 1 1
## 21242 21249 21252 21253 21258 21262 21269 21272 21276 21285
## 1 1 1 1 1 1 1 1 1 1
## 21292 21310 21317 21321 21322 21325 21332 21333 21345 21349
## 1 1 1 1 1 1 1 1 1 1
## 21364 21373 21380 21382 21392 21393 21407 21411 21438 21439
## 1 1 1 1 1 1 1 1 1 1
## 21442 21443 21449 21450 21459 21460 21467 21486 21488 21526
## 1 1 1 4 1 1 1 1 1 1
## 21529 21539 21543 21552 21563 21568 21569 21573 21579 21584
## 1 1 1 1 1 1 1 3 1 1
## 21585 21588 21599 21601 21622 21625 21629 21643 21651 21661
## 1 1 1 1 1 1 1 1 1 1
## 21675 21683 21692 21694 21696 21699 21702 21707 21709 21712
## 1 2 1 2 2 1 1 1 1 2
## 21715 21733 21736 21744 21756 21757 21767 21794 21795 21803
## 1 1 1 1 1 1 1 1 1 1
## 21813 21823 21824 21852 21861 21877 21879 21880 21883 21886
## 1 1 1 1 1 1 1 1 1 1
## 21890 21905 21921 21927 21936 21941 21943 21948 21949 21956
## 1 1 1 1 1 1 1 1 1 2
## 21959 21964 21965 21968 21969 21974 21977 21978 21987 21996
## 1 1 1 1 1 1 1 1 1 2
## 22026 22030 22032 22039 22045 22046 22056 22060 22062 22063
## 1 3 1 1 1 1 1 1 2 2
## 22067 22069 22073 22083 22086 22101 22104 22106 22114 22116
## 1 1 2 1 1 1 2 1 1 1
## 22131 22137 22144 22152 22169 22185 22188 22218 22225 22246
## 2 1 1 2 1 1 1 1 1 1
## 22248 22250 22267 22277 22280 22290 22292 22300 22319 22336
## 1 3 1 1 1 1 1 1 1 1
## 22350 22364 22369 22386 22394 22418 22425 22430 22432 22450
## 1 1 1 1 1 1 1 1 1 1
## 22461 22467 22471 22475 22483 22489 22493 22497 22500 22546
## 1 1 1 1 2 1 1 3 1 1
## 22560 22562 22565 22569 22571 22585 22591 22598 22600 22602
## 1 1 1 2 1 1 1 1 1 1
## 22611 22620 22625 22630 22631 22660 22665 22671 22686 22691
## 1 1 1 1 1 1 1 1 1 1
## 22700 22706 22707 22709 22716 22722 22739 22763 22781 22782
## 1 1 1 1 1 1 1 1 1 1
## 22796 22799 22802 22818 22822 22823 22824 22825 22831 22871
## 1 1 1 1 2 1 1 1 1 1
## 22872 22873 22877 22885 22886 22891 22900 22911 22925 22927
## 1 1 1 1 1 2 1 1 1 1
## 22929 22962 22973 22974 22979 22985 23002 23007 23014 23023
## 1 1 1 1 1 1 1 1 1 1
## 23046 23053 23061 23080 23084 23098 23104 23130 23141 23148
## 1 1 1 1 1 1 2 1 2 1
## 23149 23150 23156 23173 23176 23179 23182 23189 23195 23208
## 1 1 1 1 2 1 1 1 1 1
## 23209 23231 23241 23244 23246 23249 23250 23257 23272 23279
## 1 1 1 1 1 1 1 1 1 1
## 23284 23286 23295 23296 23303 23304 23305 23308 23313 23320
## 1 1 1 1 1 1 1 1 1 1
## 23322 23341 23354 23361 23372 23377 23384 23385 23397 23403
## 1 1 1 1 1 1 1 1 1 2
## 23409 23419 23428 23440 23441 23443 23444 23469 23476 23489
## 1 1 1 1 1 1 1 1 1 1
## 23495 23506 23527 23545 23549 23552 23553 23572 23581 23604
## 1 1 1 1 1 1 1 1 1 1
## 23608 23609 23610 23627 23628 23650 23693 23703 23706 23712
## 2 1 1 1 2 1 1 1 1 1
## 23720 23730 23739 23742 23752 23786 23790 23847 23850 23851
## 1 1 1 1 1 1 1 1 1 1
## 23856 23861 23881 23893 23896 23902 23905 23909 23910 23915
## 1 1 1 1 1 1 1 1 1 1
## 23930 23936 23954 23957 23969 23976 23983 23990 23994 24000
## 1 1 1 1 1 1 1 1 1 1
## 24007 24010 24027 24039 24062 24067 24074 24079 24080 24085
## 1 1 1 1 1 2 1 1 1 1
## 24095 24110 24114 24116 24129 24135 24136 24142 24143 24169
## 1 1 2 1 1 1 1 1 1 2
## 24174 24205 24207 24209 24226 24240 24245 24260 24264 24280
## 1 1 1 1 1 1 1 1 1 1
## 24290 24291 24296 24301 24318 24324 24328 24344 24366 24367
## 1 1 1 1 1 1 1 2 1 1
## 24379 24390 24392 24395 24396 24403 24404 24414 24425 24441
## 1 1 1 1 1 1 1 1 1 1
## 24454 24476 24478 24486 24504 24508 24523 24525 24545 24548
## 1 1 1 1 2 1 1 1 1 1
## 24572 24580 24582 24595 24603 24611 24612 24614 24616 24642
## 1 1 2 1 1 1 1 1 1 2
## 24647 24672 24680 24686 24693 24696 24698 24703 24728 24730
## 1 1 1 1 1 1 2 2 1 1
## 24733 24755 24769 24776 24781 24791 24807 24811 24823 24835
## 1 1 1 2 1 1 1 1 1 1
## 24847 24864 24871 24874 24900 24912 24920 24955 24960 24964
## 1 1 1 1 1 1 1 1 1 1
## 24970 24974 24980 24991 25000 25002 25004 25010 25022 25047
## 1 1 1 1 1 1 1 1 1 1
## 25054 25070 25072 25082 25083 25095 25096 25100 25127 25135
## 1 1 1 1 1 1 1 1 1 1
## 25138 25139 25155 25161 25166 25200 25203 25208 25215 25218
## 1 1 1 1 1 1 1 1 1 1
## 25225 25228 25231 25234 25235 25240 25241 25249 25254 25283
## 1 1 2 1 1 1 1 2 1 1
## 25284 25285 25292 25298 25305 25308 25328 25330 25340 25357
## 1 1 1 1 1 1 1 1 1 1
## 25364 25393 25395 25405 25421 25457 25463 25464 25473 25475
## 1 1 1 1 1 1 1 1 1 1
## 25492 25506 25509 25518 25549 25559 25562 25566 25573 25591
## 1 1 1 1 1 1 1 1 3 1
## 25602 25621 25653 25668 25670 25672 25740 25741 25785 25796
## 1 1 1 1 1 1 1 1 1 1
## 25808 25823 25826 25827 25829 25837 25842 25856 25864 25902
## 1 1 1 2 1 1 1 1 1 1
## 25937 25950 25978 25984 25994 26000 26009 26040 26043 26047
## 1 1 1 1 1 1 1 1 1 2
## 26074 26075 26099 26114 26118 26143 26164 26166 26184 26186
## 1 1 1 1 3 1 1 1 1 1
## 26202 26205 26211 26221 26222 26229 26239 26247 26293 26300
## 1 1 1 1 1 1 1 1 1 1
## 26346 26400 26413 26414 26421 26453 26511 26514 26532 26542
## 1 1 1 1 1 1 1 1 1 1
## 26575 26579 26588 26591 26634 26660 26661 26683 26691 26700
## 1 1 1 1 1 1 1 2 1 2
## 26711 26720 26737 26751 26770 26819 26836 26840 26841 26845
## 1 1 1 1 1 1 1 1 1 1
## 26854 26855 26858 26861 26873 26882 26917 26919 26964 26991
## 1 1 1 1 1 1 1 1 2 1
## 26992 26995 27002 27021 27023 27025 27028 27049 27059 27060
## 1 1 1 2 1 1 1 1 1 1
## 27065 27068 27099 27102 27130 27140 27147 27168 27184 27192
## 1 3 1 1 1 1 1 1 1 1
## 27195 27241 27256 27260 27273 27354 27363 27429 27436 27454
## 1 1 1 1 1 1 1 1 1 1
## 27478 27529 27530 27538 27540 27554 27569 27583 27590 27594
## 2 2 1 1 1 1 1 1 1 1
## 27596 27628 27647 27671 27677 27710 27714 27722 27741 27743
## 1 1 1 1 1 1 1 1 1 1
## 27755 27786 27821 27826 27829 27836 27852 27853 27874 27887
## 1 1 1 1 1 1 2 1 1 2
## 27888 27926 27929 27945 27975 27980 27991 28056 28059 28105
## 1 2 1 1 1 1 1 1 1 2
## 28123 28184 28202 28238 28281 28282 28285 28286 28287 28289
## 1 1 1 1 1 1 1 1 1 1
## 28299 28321 28326 28343 28348 28357 28406 28418 28419 28446
## 1 1 1 1 1 1 1 1 1 1
## 28471 28486 28522 28525 28547 28549 28557 28561 28566 28618
## 1 1 1 1 1 1 1 1 1 1
## 28654 28679 28691 28707 28709 28740 28750 28760 28812 28867
## 1 1 1 1 1 1 1 1 1 2
## 28878 28902 28921 28929 28945 28970 29033 29064 29082 29083
## 1 1 1 2 1 2 1 1 1 1
## 29104 29120 29123 29125 29145 29165 29197 29200 29203 29256
## 1 1 2 1 1 1 2 1 1 1
## 29277 29305 29391 29396 29407 29409 29412 29455 29465 29481
## 1 1 1 1 1 1 1 1 1 1
## 29482 29490 29492 29525 29531 29533 29580 29590 29600 29647
## 1 1 1 1 1 1 1 1 1 1
## 29670 29684 29699 29776 29794 29802 29803 29826 29888 29942
## 1 1 1 1 1 1 1 1 1 1
## 30004 30055 30065 30078 30092 30105 30108 30115 30121 30143
## 1 1 1 1 2 1 1 1 1 2
## 30171 30182 30209 30211 30213 30249 30271 30329 30342 30371
## 2 1 1 1 1 1 1 1 1 1
## 30385 30426 30468 30477 30480 30498 30547 30628 30641 30698
## 1 1 1 1 1 1 1 1 1 1
## 30723 30728 30730 30741 30746 30800 30806 30821 30842 30911
## 1 1 1 1 1 1 1 1 1 1
## 30913 30928 30938 30962 31062 31077 31082 31163 31165 31181
## 1 1 1 1 1 1 1 1 1 2
## 31187 31225 31235 31247 31344 31360 31389 31438 31459 31460
## 1 1 1 1 1 1 1 2 1 1
## 31463 31482 31568 31604 31626 31644 31663 31668 31717 31725
## 1 1 2 1 1 1 1 1 1 1
## 31745 31834 31855 31953 31964 32032 32045 32077 32101 32217
## 1 1 1 1 1 1 1 1 1 1
## 32256 32314 32329 32336 32369 32383 32400 32435 32438 32452
## 1 1 1 1 1 1 1 1 1 1
## 32470 32491 32509 32516 32547 32605 32656 32694 32733 32746
## 1 1 1 1 1 1 1 1 1 1
## 32807 32847 32914 32918 32920 33000 33024 33082 33092 33095
## 1 1 1 1 1 1 1 1 1 1
## 33132 33146 33162 33244 33344 33361 33422 33450 33474 33558
## 1 1 1 2 3 1 1 1 1 2
## 33565 33594 33624 33644 33704 33743 33751 33797 33846 33869
## 1 1 1 1 1 1 1 1 1 1
## 33897 33905 34021 34027 34058 34088 34107 34146 34154 34171
## 1 1 1 1 1 1 1 1 1 1
## 34183 34227 34230 34270 34339 34342 34365 34399 34415 34439
## 1 1 1 1 1 1 1 1 1 1
## 34446 34449 34450 34469 34555 34574 34580 34643 34746 34753
## 1 1 2 1 1 1 1 1 1 1
## 34770 34817 34827 34874 34910 34977 35012 35078 35096 35141
## 1 1 1 1 1 1 1 1 1 1
## 35294 35460 35476 35521 35524 35596 35602 35622 35679 35760
## 1 1 1 1 1 1 1 1 1 1
## 35770 35772 35786 35827 35915 35928 35949 36007 36012 36032
## 1 1 1 1 1 1 1 1 2 1
## 36050 36107 36127 36144 36174 36253 36293 36295 36301 36367
## 1 1 1 1 2 2 1 1 1 1
## 36477 36480 36542 36592 36746 36751 36786 36828 36953 36994
## 1 1 1 1 1 1 1 1 1 1
## 37013 37051 37084 37128 37223 37226 37231 37246 37247 37310
## 1 1 1 1 1 2 1 1 1 1
## 37489 37521 37604 37618 37649 37677 37686 37701 37825 37911
## 1 1 1 1 1 1 1 1 1 1
## 37944 37967 38004 38066 38098 38159 38335 38457 38486 38491
## 1 1 1 1 1 1 1 1 1 1
## 38542 38561 38700 38707 38811 38848 39143 39515 39758 39794
## 2 1 1 1 1 1 1 1 1 1
## 39828 40035 40155 40170 40204 40395 40593 40740 40848 40912
## 1 3 1 1 1 1 1 1 1 1
## 41338 41361 41466 41609 41732 41770 42169 42218 42305 42344
## 1 1 1 1 1 2 1 1 1 1
## 42368 42450 42452 42473 42497 42546 42699 42800 42808 42876
## 1 1 1 1 1 1 1 1 1 1
## 43058 43084 43099 43128 43234 43259 43433 43646 43678 43684
## 1 1 2 1 1 1 1 1 1 1
## 43729 43846 44092 44347 44460 44487 44539 44548 44616 44899
## 1 1 1 1 1 1 1 1 1 1
## 44947 44964 45060 45126 45473 45532 45590 45633 45681 45692
## 1 1 1 1 1 1 1 1 1 1
## 45785 45797 45973 46083 46244 46315 46358 46541 46605 46843
## 1 1 1 1 1 1 2 1 1 1
## 46886 47324 47409 47465 47553 47561 47752 47827 48163 48327
## 1 1 1 1 2 1 1 1 1 1
## 48708 48943 49016 49223 49242 49411 49442 49445 49517 49558
## 1 1 1 1 1 1 1 1 1 1
## 49616 49657 49807 50023 50112 50698 50753 50915 51105 51163
## 1 1 1 1 1 1 1 1 1 1
## 51203 51262 51369 51525 51620 51722 51970 51985 52057 52200
## 1 1 1 1 1 1 1 1 1 1
## 52239 52313 52741 52925 53053 53161 53290 53331 53528 53769
## 1 1 1 1 1 1 1 1 1 1
## 53913 54067 54789 55165 55353 55494 55796 55820 55859 55993
## 1 1 1 1 1 2 1 1 1 1
## 56180 56200 56866 56900 57085 57518 57750 58370 58680 59000
## 1 1 1 1 1 1 1 1 1 2
## 59032 59043 59120 59432 59692 60014 60621 60743 61381 62038
## 1 1 1 1 1 1 1 1 1 1
## 62682 63059 63276 64208 64297 67559 68320 68935 70338 70796
## 1 1 1 1 1 1 3 1 1 1
## 72636 74185 74187 74390 77177 78991 79000 85503 89072 91445
## 1 1 1 1 1 1 1 1 1 1
## 93153 95172 97124 97992 99834 100000 107114 107515 109970 122775
## 1 1 1 1 1 1 1 1 1 1
## 130444 135663 143092 143174 155969 190989 194050 286344 377000 781251
## 1 1 1 2 1 1 1 1 1 1here we observe that 10 % of values are 0. these are look like transcations .
finding the less than value 9000 & 25000
NROW(data[which(data$x234<=9000),])## [1] 73771here we observed 73% less than 9000
NROW(data[which(data$x234<=25000),])## [1] 80083here we observed 80% are less than 25000
Now visualize the statistics
Histogram
hist(data$x234,
main = "Histogram of x234",
xlab = "x234")
abline(v =mean(data$x234, na.rm = TRUE),col = "red",
lwd = 2)
## observe that on the x axis the values are more so its shows in the approx,these are like transcations amount i am not sure.
Boxplot
boxplot(data$x234,
main = toupper("Boxplot of x234"),
ylab = "x234",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x234, na.rm = TRUE)
plot(d, main = "Kernel density of x234")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x234, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observe that 0 has 10% of density there is 20% of missing values and the bandwidth is 231,the skewness is a postive because the tail will have the large values,so the variable has descrete values.
summary of the variable x235
summary(data$x235)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 1000 5500 10207 16000 250000 20083observe that values from 0 to 25000, median is 0 & mean is 10207
Description of the variable x235
describe(data$x235)## data$x235
## n missing distinct Info Mean Gmd .05 .10
## 79917 20083 2947 0.999 10207 11902 300 300
## .25 .50 .75 .90 .95
## 1000 5500 16000 25800 32000
##
## lowest : 0 50 56 58 64, highest: 132400 150000 160000 180000 250000Here the kurtosis is positive and it has large data in tails.
Total number of values excluding NA
NROW(data$x235) - NROW(data[which(is.na(data$x235)),])## [1] 79917Here the value is 79917 because there is 21% of missing values.
table(data$x235)##
## 0 50 56 58 64 68 70 75 78 81
## 86 3 1 1 1 1 6 4 1 1
## 86 88 89 100 101 102 103 107 109 110
## 1 1 1 170 1 1 1 1 1 3
## 111 115 116 117 118 120 122 124 125 130
## 1 2 1 1 1 3 1 90 9 46
## 133 135 136 137 138 140 141 142 143 149
## 1 6 3 1 1 4 1 1 1 1
## 150 154 156 157 158 160 168 170 171 174
## 92 3 1 1 1 5 1 2 2 3
## 177 180 181 182 184 189 190 195 196 198
## 1 3 1 1 1 1 10 1 1 1
## 199 200 201 202 206 209 210 211 212 215
## 1 821 26 1 2 1 4 2 1 2
## 217 220 224 225 226 227 229 230 231 232
## 1 5 9 2 1 1 3 9 1 1
## 233 234 235 240 245 246 247 248 250 251
## 1 1 1 4 2 1 1 1 662 9
## 252 253 254 255 256 258 259 260 261 264
## 1 2 1 2 1 1 1 5 1 1
## 266 269 270 273 275 277 278 280 281 283
## 2 1 4 1 7 2 1 3 1 4
## 285 290 292 294 295 297 298 299 300 301
## 1 3 1 1 1 2 1 1 6108 13
## 302 303 304 305 306 307 308 309 310 311
## 4 2 6 9 2 1 2 1 6 1
## 315 316 317 318 319 320 321 322 323 324
## 1 1 2 1 2 13 4 1 4 3
## 325 326 327 328 329 330 331 332 333 334
## 12 2 2 3 5 4 1 3 3 1
## 335 336 337 338 339 340 341 342 344 345
## 4 2 2 1 2 4 4 1 4 2
## 346 348 350 351 354 355 356 359 360 362
## 2 1 206 6 3 1 1 1 6 2
## 363 364 365 366 367 369 370 371 373 374
## 1 1 3 1 1 3 2 1 1 3
## 375 376 379 380 382 386 388 390 391 392
## 23 2 1 7 1 2 1 6 1 1
## 394 398 399 400 401 403 404 405 406 407
## 4 1 1 1477 5 2 2 3 3 1
## 409 410 411 412 413 414 416 417 418 419
## 2 4 2 1 2 1 1 2 1 1
## 423 424 425 426 428 430 431 433 435 437
## 3 9 28 2 1 6 1 4 3 2
## 440 442 445 446 447 448 449 450 451 453
## 4 1 1 1 2 2 1 175 4 2
## 454 458 460 461 462 463 464 467 470 473
## 1 2 5 1 1 1 1 1 5 1
## 474 475 476 477 479 480 482 483 487 488
## 1 18 4 2 3 8 1 2 2 1
## 489 490 495 496 499 500 501 503 504 505
## 2 4 2 3 4 3654 32 4 1 9
## 506 507 508 510 511 514 515 518 520 523
## 3 3 2 9 2 2 1 2 3 2
## 524 525 526 527 530 531 532 533 535 539
## 2 11 2 2 6 1 3 1 2 1
## 540 541 542 543 545 546 547 548 549 550
## 2 2 3 1 1 1 3 2 1 216
## 551 553 558 559 560 561 562 563 565 567
## 5 2 1 1 1 1 2 1 1 2
## 568 570 571 574 575 576 577 578 579 580
## 2 6 1 1 7 3 1 1 1 3
## 581 582 586 588 590 591 592 593 596 597
## 1 2 2 2 2 1 1 1 1 1
## 598 599 600 601 606 607 610 611 612 613
## 1 5 729 12 1 1 5 1 2 1
## 618 619 620 624 625 626 628 629 630 633
## 2 1 3 2 18 1 1 2 9 2
## 634 635 636 637 638 639 640 643 644 646
## 1 2 2 1 2 2 5 1 2 1
## 648 650 651 655 656 657 659 660 663 668
## 2 151 6 1 2 1 1 10 1 1
## 672 673 674 675 680 683 685 690 692 693
## 1 2 1 12 5 2 1 4 2 1
## 696 698 700 701 702 705 709 710 716 717
## 1 3 1287 4 2 2 1 4 1 1
## 718 719 720 721 722 723 724 725 727 728
## 2 1 3 1 2 1 2 1 1 1
## 729 730 732 735 736 737 740 741 744 745
## 1 4 1 1 1 1 3 1 1 2
## 747 748 749 750 751 752 753 756 759 760
## 1 1 1 760 6 1 1 2 1 1
## 761 762 765 768 769 770 771 772 773 775
## 1 1 3 2 2 3 1 1 2 7
## 776 778 780 783 784 785 786 787 788 790
## 2 1 6 1 1 1 3 2 1 3
## 792 793 795 796 800 801 805 806 807 810
## 4 3 1 2 720 3 2 1 1 3
## 815 817 818 819 820 821 822 825 827 829
## 3 1 1 3 2 1 2 4 2 2
## 830 833 840 841 843 845 846 847 849 850
## 1 1 5 1 1 1 1 1 1 98
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## 48800 48890 48900 49000 49100 49130 49200 49300 49400 49500
## 6 2 5 11 4 1 7 2 11 14
## 49600 49650 49700 49720 49800 49900 49940 49960 50000 50100
## 6 1 15 1 11 8 1 1 350 5
## 50200 50300 50400 50500 50600 50650 50700 50800 50890 50900
## 1 3 6 1 1 1 2 3 1 5
## 51000 51050 51100 51110 51170 51200 51300 51400 51500 51600
## 3 1 3 1 1 3 1 3 2 4
## 51700 51800 51900 51905 52000 52200 52300 52400 52500 52600
## 1 1 6 1 8 13 1 2 4 5
## 52740 52800 52900 52940 53000 53100 53180 53200 53300 53400
## 1 8 1 1 6 1 1 2 2 3
## 53500 53600 53700 53800 54000 54200 54400 54410 54500 54600
## 1 4 3 3 9 1 3 1 1 2
## 54700 54730 54800 54900 54930 55000 55200 55400 55450 55800
## 2 1 1 1 1 27 1 2 1 2
## 56000 56100 56200 56220 56400 56500 56550 56600 56700 56900
## 4 5 7 1 2 9 1 4 4 3
## 57000 57200 57500 57600 57800 57900 58000 58100 58300 58400
## 4 4 20 42 2 2 2 3 1 3
## 58500 58600 59000 59072 59100 59200 59300 59400 59500 59700
## 3 2 6 1 1 1 1 3 2 2
## 60000 60014 60200 60500 60900 61000 61100 61200 61300 61903
## 52 1 1 3 2 3 1 1 1 1
## 61948 62100 62168 62300 62400 62500 62600 63000 63200 63300
## 1 2 1 1 1 1 2 4 3 1
## 63500 63600 63800 63900 64000 64100 64300 64800 65000 65400
## 1 1 1 2 2 1 2 2 11 1
## 65500 65800 65900 66000 66300 66400 66500 66700 67000 67100
## 2 1 2 2 1 1 2 2 1 1
## 68100 68600 68900 69000 69200 69500 70000 70590 70700 70956
## 1 2 1 1 1 1 15 1 1 1
## 71200 71500 72000 72500 73400 73500 73900 74185 74500 74600
## 1 1 3 1 1 1 1 1 2 1
## 75000 75100 75900 77000 77300 77800 78000 79000 80000 80500
## 36 1 1 2 1 1 3 2 8 1
## 81900 84500 85000 86300 86600 88000 88200 88400 88500 88700
## 1 1 4 1 2 1 1 1 1 1
## 90000 93100 95000 99999 100000 101100 102100 103100 103900 104000
## 1 2 3 1 47 1 1 1 1 1
## 104599 108000 109000 109500 110000 110300 115000 116300 120000 120700
## 2 1 1 1 1 1 2 1 2 1
## 121700 125000 132400 150000 160000 180000 250000
## 1 1 1 3 1 2 2finding the less than value 45000 & 100000
NROW(data[which(data$x235<=45000),])## [1] 78594here we observed 78% less than 45000
NROW(data[which(data$x235<=100000),])## [1] 79888here we observed 79% are less than 100000
Now visualize the statistics
Histogram
hist(data$x235,
main = "Histogram of x235",
xlab = "x235")
abline(v =mean(data$x235, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 50000 has max values.
Boxplot
boxplot(data$x235,
main = toupper("Boxplot of x235"),
ylab = "x235",
col = "red")
## we got the outliers
Density Plot
d <- density(data$x235, na.rm = TRUE)
plot(d, main = "Kernel density of x235")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x235, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 to 50000 has more density there is 21% of missing values most of the values are lies in b0 to 50000 and the skewness is a postive because the tail will have the large values, so the variable has descrete values.
summary of the variable x236
summary(data$x236)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -50030 0 262 9323 9700 320085observe that values from -50030 to 320085, median is 262 & mean is 9323
Description of the variable x236
describe(data$x236)## data$x236
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 27954 0.946 9323 14973 -19 0
## .25 .50 .75 .90 .95
## 0 262 9700 31450 49270
##
## lowest : -50030 -36483 -24990 -22651 -10576, highest: 265129 288049 292351 312144 320085Here the kurtosis is positive and it has large data in tail.
Total number of values excluding NA
NROW(data$x236) - NROW(data[which(is.na(data$x236)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x236)##
## -50030 -36483 -24990 -22651 -10576 -9450 -8289 -7858 -7247 -7038
## 1 1 1 1 1 1 1 1 1 1
## -6803 -5685 -5304 -5279 -5122 -4941 -4789 -4551 -4114 -4055
## 1 1 1 1 1 1 1 1 1 1
## -3766 -3708 -3644 -3599 -3534 -3526 -3422 -3192 -3110 -3056
## 1 2 1 1 1 1 1 1 1 1
## -2940 -2891 -2784 -2668 -2661 -2642 -2635 -2630 -2620 -2525
## 1 1 1 1 1 1 1 1 1 2
## -2509 -2438 -2340 -2333 -2266 -2206 -2190 -2169 -2120 -2073
## 1 1 1 1 1 1 1 2 1 1
## -2055 -2049 -2039 -2027 -1986 -1945 -1909 -1892 -1876 -1868
## 1 1 1 1 1 1 1 1 1 2
## -1857 -1803 -1788 -1781 -1767 -1763 -1757 -1756 -1751 -1732
## 1 1 1 1 1 1 1 1 1 2
## -1729 -1725 -1715 -1711 -1701 -1687 -1674 -1665 -1636 -1614
## 1 1 1 1 1 1 1 1 1 1
## -1584 -1578 -1567 -1563 -1540 -1529 -1474 -1471 -1458 -1457
## 1 1 1 1 1 1 1 1 1 1
## -1447 -1441 -1437 -1426 -1421 -1418 -1417 -1409 -1390 -1377
## 1 1 1 1 1 1 1 3 1 1
## -1357 -1349 -1346 -1319 -1315 -1313 -1310 -1309 -1300 -1298
## 1 1 1 1 1 1 1 1 1 1
## -1291 -1287 -1278 -1274 -1264 -1259 -1257 -1239 -1237 -1236
## 1 1 1 1 1 1 2 1 1 1
## -1232 -1228 -1223 -1216 -1214 -1198 -1197 -1185 -1181 -1173
## 1 1 1 1 1 1 1 1 1 2
## -1172 -1161 -1160 -1156 -1153 -1152 -1149 -1143 -1138 -1130
## 1 1 1 1 1 1 1 1 1 1
## -1129 -1127 -1120 -1095 -1075 -1062 -1061 -1050 -1049 -1047
## 1 1 1 1 1 1 2 1 1 1
## -1037 -1035 -1034 -1030 -1019 -1015 -1014 -1013 -1012 -1010
## 1 2 1 1 1 1 1 1 1 1
## -1003 -1002 -1000 -999 -997 -995 -993 -985 -979 -978
## 1 1 1 1 1 2 1 1 1 1
## -976 -975 -973 -971 -970 -960 -956 -955 -951 -949
## 1 1 2 1 1 1 1 2 2 2
## -946 -943 -942 -941 -940 -932 -930 -929 -928 -921
## 1 1 1 1 1 1 1 1 1 1
## -920 -918 -914 -911 -903 -900 -892 -890 -889 -887
## 1 2 1 1 1 1 1 1 1 1
## -885 -882 -878 -867 -864 -860 -857 -851 -850 -847
## 1 1 1 1 1 2 2 2 1 2
## -845 -843 -841 -838 -832 -830 -824 -821 -817 -815
## 1 1 1 2 1 1 1 1 1 1
## -813 -812 -807 -806 -805 -804 -800 -796 -791 -788
## 1 1 1 1 2 3 1 1 1 2
## -785 -784 -781 -779 -777 -773 -766 -763 -761 -756
## 1 3 2 1 2 1 1 1 1 1
## -753 -748 -747 -746 -739 -735 -734 -733 -731 -728
## 1 1 1 1 1 2 1 2 2 1
## -727 -726 -725 -721 -720 -718 -717 -716 -714 -712
## 2 1 2 1 2 2 1 2 1 1
## -709 -708 -707 -705 -704 -702 -701 -698 -696 -695
## 2 1 2 1 1 1 2 1 2 1
## -694 -693 -691 -690 -689 -687 -686 -685 -684 -683
## 1 1 1 1 2 1 1 1 1 2
## -682 -681 -680 -677 -670 -669 -665 -664 -662 -661
## 2 1 1 1 1 1 1 1 1 1
## -660 -659 -658 -656 -653 -652 -651 -649 -647 -645
## 2 2 2 1 2 1 1 2 1 2
## -640 -636 -634 -633 -631 -629 -627 -626 -625 -624
## 1 2 2 1 2 2 1 1 1 2
## -623 -622 -620 -618 -616 -615 -614 -612 -610 -609
## 1 1 1 2 1 1 5 1 1 1
## -608 -607 -606 -605 -604 -603 -602 -601 -600 -599
## 2 1 2 2 2 1 2 1 2 2
## -598 -596 -595 -593 -592 -589 -588 -587 -586 -585
## 1 1 2 1 3 1 1 1 1 2
## -582 -580 -577 -576 -575 -573 -571 -570 -568 -567
## 1 1 2 2 3 2 1 2 1 1
## -566 -564 -563 -561 -559 -558 -557 -556 -555 -554
## 1 2 1 1 2 1 1 2 1 3
## -553 -552 -551 -550 -547 -546 -545 -544 -542 -541
## 1 2 2 1 1 2 1 1 2 2
## -540 -539 -538 -537 -535 -534 -532 -530 -529 -528
## 1 1 2 6 1 2 2 2 2 1
## -527 -522 -521 -519 -517 -516 -515 -514 -512 -511
## 2 1 3 3 3 1 1 1 1 2
## -510 -507 -505 -504 -501 -500 -498 -497 -496 -495
## 2 2 3 3 2 1 1 1 1 1
## -494 -493 -492 -491 -489 -488 -486 -485 -484 -483
## 1 1 2 4 3 3 2 1 3 1
## -480 -478 -477 -475 -474 -473 -472 -471 -470 -468
## 2 5 3 3 1 1 1 1 3 3
## -467 -465 -464 -463 -462 -461 -460 -458 -457 -456
## 3 2 1 2 2 3 3 3 1 2
## -455 -454 -453 -452 -451 -450 -449 -448 -447 -446
## 1 1 4 1 3 5 1 2 1 3
## -444 -443 -442 -441 -440 -439 -436 -435 -434 -432
## 2 2 1 3 4 3 2 1 5 1
## -431 -430 -429 -428 -427 -426 -425 -424 -423 -422
## 2 7 4 2 4 5 1 3 2 3
## -421 -420 -419 -417 -416 -415 -413 -412 -411 -410
## 1 1 4 2 2 1 1 1 1 2
## -409 -408 -407 -406 -404 -403 -402 -400 -399 -398
## 2 5 4 3 3 4 3 2 4 4
## -397 -396 -395 -394 -392 -391 -390 -389 -388 -387
## 2 2 1 3 3 4 7 7 1 1
## -386 -385 -383 -382 -381 -380 -379 -378 -377 -376
## 2 3 4 1 5 2 1 4 1 2
## -375 -374 -373 -372 -371 -370 -369 -368 -367 -366
## 2 1 3 3 1 6 1 2 3 3
## -365 -364 -363 -362 -360 -359 -358 -357 -356 -355
## 3 3 5 2 3 2 3 1 4 1
## -354 -353 -351 -350 -349 -348 -347 -346 -345 -344
## 3 3 7 2 2 4 3 6 3 6
## -343 -342 -341 -340 -339 -338 -337 -336 -335 -334
## 1 2 2 3 2 4 1 3 2 4
## -333 -332 -331 -330 -329 -328 -327 -326 -325 -324
## 5 3 4 4 6 7 3 2 3 4
## -323 -322 -321 -320 -319 -318 -317 -315 -314 -313
## 3 5 1 2 3 3 5 8 1 4
## -312 -311 -310 -309 -308 -307 -306 -305 -304 -303
## 4 2 6 9 6 3 6 5 2 2
## -302 -301 -300 -299 -298 -297 -296 -295 -294 -293
## 2 6 3 3 2 2 5 7 5 7
## -292 -291 -290 -289 -288 -287 -286 -285 -284 -283
## 4 6 5 8 3 5 5 7 11 2
## -282 -281 -280 -279 -278 -277 -276 -275 -274 -273
## 4 8 8 3 1 3 6 3 4 4
## -272 -271 -270 -269 -268 -267 -266 -265 -264 -263
## 7 3 6 6 7 4 6 7 10 4
## -262 -261 -260 -259 -258 -257 -256 -255 -254 -253
## 3 5 9 3 4 5 7 7 4 4
## -252 -251 -250 -249 -248 -247 -246 -245 -244 -243
## 7 5 5 6 10 5 7 4 5 6
## -242 -241 -240 -239 -238 -237 -236 -235 -234 -233
## 3 4 7 6 6 4 9 7 7 7
## -232 -231 -230 -229 -228 -227 -226 -225 -224 -223
## 6 5 8 5 1 7 7 4 1 9
## -222 -221 -220 -219 -218 -217 -216 -215 -214 -213
## 6 3 2 4 4 5 9 7 6 7
## -212 -211 -210 -209 -208 -207 -206 -205 -204 -203
## 3 9 7 8 6 7 9 10 9 12
## -202 -201 -200 -199 -198 -197 -196 -195 -194 -193
## 6 8 12 8 5 14 13 3 13 6
## -192 -191 -190 -189 -188 -187 -186 -185 -184 -183
## 10 18 11 9 10 14 12 11 7 16
## -182 -181 -180 -179 -178 -177 -176 -175 -174 -173
## 5 6 11 4 4 10 14 7 13 7
## -172 -171 -170 -169 -168 -167 -166 -165 -164 -163
## 14 15 5 16 9 10 8 14 13 19
## -162 -161 -160 -159 -158 -157 -156 -155 -154 -153
## 16 12 16 15 7 11 8 13 9 14
## -152 -151 -150 -149 -148 -147 -146 -145 -144 -143
## 13 16 15 15 13 9 9 10 12 21
## -142 -141 -140 -139 -138 -137 -136 -135 -134 -133
## 27 18 21 23 18 20 15 18 26 28
## -132 -131 -130 -129 -128 -127 -126 -125 -124 -123
## 21 15 24 21 13 14 17 25 16 19
## -122 -121 -120 -119 -118 -117 -116 -115 -114 -113
## 20 20 24 29 22 18 16 20 24 18
## -112 -111 -110 -109 -108 -107 -106 -105 -104 -103
## 13 17 14 21 14 15 12 11 16 24
## -102 -101 -100 -99 -98 -97 -96 -95 -94 -93
## 25 28 21 15 15 20 20 22 27 21
## -92 -91 -90 -89 -88 -87 -86 -85 -84 -83
## 20 23 23 23 24 24 17 24 22 21
## -82 -81 -80 -79 -78 -77 -76 -75 -74 -73
## 24 24 25 22 30 20 28 15 20 15
## -72 -71 -70 -69 -68 -67 -66 -65 -64 -63
## 27 14 17 26 25 20 19 25 14 15
## -62 -61 -60 -59 -58 -57 -56 -55 -54 -53
## 19 18 19 23 20 18 19 19 20 17
## -52 -51 -50 -49 -48 -47 -46 -45 -44 -43
## 19 15 20 19 26 22 25 23 27 38
## -42 -41 -40 -39 -38 -37 -36 -35 -34 -33
## 27 20 29 27 33 42 35 36 36 34
## -32 -31 -30 -29 -28 -27 -26 -25 -24 -23
## 36 37 40 29 35 16 32 29 32 27
## -22 -21 -20 -19 -18 -17 -16 -15 -14 -13
## 30 25 35 20 21 34 26 33 31 44
## -12 -11 -10 -9 -8 -7 -6 -5 -4 -3
## 28 34 51 38 32 42 51 58 46 61
## -2 -1 0 1 2 3 4 5 6 7
## 50 52 37802 78 60 51 52 45 52 34
## 8 9 10 11 12 13 14 15 16 17
## 47 46 45 42 34 42 51 44 41 47
## 18 19 20 21 22 23 24 25 26 27
## 48 42 29 34 48 43 51 39 34 33
## 28 29 30 31 32 33 34 35 36 37
## 30 42 35 39 42 38 43 34 39 39
## 38 39 40 41 42 43 44 45 46 47
## 25 39 34 23 27 41 38 42 22 38
## 48 49 50 51 52 53 54 55 56 57
## 31 39 34 40 32 29 34 36 23 29
## 58 59 60 61 62 63 64 65 66 67
## 32 23 27 31 27 18 33 27 26 23
## 68 69 70 71 72 73 74 75 76 77
## 30 23 23 17 34 25 26 24 18 28
## 78 79 80 81 82 83 84 85 86 87
## 33 28 17 25 22 26 18 19 24 12
## 88 89 90 91 92 93 94 95 96 97
## 16 18 24 22 29 22 24 18 28 24
## 98 99 100 101 102 103 104 105 106 107
## 18 26 68 22 23 22 22 26 27 31
## 108 109 110 111 112 113 114 115 116 117
## 19 16 12 24 24 21 19 20 20 15
## 118 119 120 121 122 123 124 125 126 127
## 27 23 12 19 24 19 22 13 26 25
## 128 129 130 131 132 133 134 135 136 137
## 28 29 18 28 23 20 8 11 22 15
## 138 139 140 141 142 143 144 145 146 147
## 22 23 19 16 14 20 19 17 15 19
## 148 149 150 151 152 153 154 155 156 157
## 19 18 35 23 16 22 12 17 17 17
## 158 159 160 161 162 163 164 165 166 167
## 20 12 16 18 11 16 21 17 16 19
## 168 169 170 171 172 173 174 175 176 177
## 23 14 17 20 15 21 20 23 15 14
## 178 179 180 181 182 183 184 185 186 187
## 10 22 19 17 13 12 16 17 22 15
## 188 189 190 191 192 193 194 195 196 197
## 15 13 19 22 14 15 23 25 19 25
## 198 199 200 201 202 203 204 205 206 207
## 37 21 167 26 20 16 12 16 14 13
## 208 209 210 211 212 213 214 215 216 217
## 5 12 13 8 14 16 13 9 21 8
## 218 219 220 221 222 223 224 225 226 227
## 17 21 22 16 10 14 10 37 19 12
## 228 229 230 231 232 233 234 235 236 237
## 11 16 17 22 12 11 20 14 14 23
## 238 239 240 241 242 243 244 245 246 247
## 10 19 14 23 15 19 14 12 13 17
## 248 249 250 251 252 253 254 255 256 257
## 15 14 127 10 14 13 16 6 18 22
## 258 259 260 261 262 263 264 265 266 267
## 11 17 13 10 12 12 16 19 16 16
## 268 269 270 271 272 273 274 275 276 277
## 16 15 16 12 16 13 12 26 19 20
## 278 279 280 281 282 283 284 285 286 287
## 12 16 13 13 13 18 5 15 11 13
## 288 289 290 291 292 293 294 295 296 297
## 9 15 17 22 4 9 23 16 21 12
## 298 299 300 301 302 303 304 305 306 307
## 14 15 436 11 13 10 8 11 10 13
## 308 309 310 311 312 313 314 315 316 317
## 18 10 11 20 12 23 12 12 14 10
## 318 319 320 321 322 323 324 325 326 327
## 16 11 20 15 11 17 15 14 11 11
## 328 329 330 331 332 333 334 335 336 337
## 13 16 8 11 16 15 11 9 4 12
## 338 339 340 341 342 343 344 345 346 347
## 9 10 15 13 9 9 11 9 14 11
## 348 349 350 351 352 353 354 355 356 357
## 15 10 28 10 8 8 12 9 8 9
## 358 359 360 361 362 363 364 365 366 367
## 11 7 10 11 9 11 12 11 18 10
## 368 369 370 371 372 373 374 375 376 377
## 15 4 10 8 6 2 7 9 14 14
## 378 379 380 381 382 383 384 385 386 387
## 7 11 10 10 10 12 11 13 10 4
## 388 389 390 391 392 393 394 395 396 397
## 13 6 9 14 6 21 15 14 12 11
## 398 399 400 401 402 403 404 405 406 407
## 10 15 127 13 7 18 9 6 9 16
## 408 409 410 411 412 413 414 415 416 417
## 13 15 12 17 7 11 8 9 11 10
## 418 419 420 421 422 423 424 425 426 427
## 18 13 8 8 16 7 12 14 6 7
## 428 429 430 431 432 433 434 435 436 437
## 15 8 6 11 8 8 11 16 7 5
## 438 439 440 441 442 443 444 445 446 447
## 14 12 11 9 9 8 17 7 12 7
## 448 449 450 451 452 453 454 455 456 457
## 9 16 16 16 9 8 9 12 10 13
## 458 459 460 461 462 463 464 465 466 467
## 14 6 3 16 10 11 13 15 14 13
## 468 469 470 471 472 473 474 475 476 477
## 12 4 10 7 8 9 9 17 12 9
## 478 479 480 481 482 483 484 485 486 487
## 7 11 11 6 9 6 11 6 9 6
## 488 489 490 491 492 493 494 495 496 497
## 10 11 11 9 12 10 12 11 9 9
## 498 499 500 501 502 503 504 505 506 507
## 14 6 324 11 8 14 10 7 11 8
## 508 509 510 511 512 513 514 515 516 517
## 5 6 9 7 11 9 12 11 11 8
## 518 519 520 521 522 523 524 525 526 527
## 9 12 11 11 18 7 2 12 5 7
## 528 529 530 531 532 533 534 535 536 537
## 11 7 9 13 3 7 12 9 4 10
## 538 539 540 541 542 543 544 545 546 547
## 9 9 10 5 9 7 6 10 6 6
## 548 549 550 551 552 553 554 555 556 557
## 9 5 21 11 12 11 7 7 11 11
## 558 559 560 561 562 563 564 565 566 567
## 6 13 9 6 6 8 11 11 6 7
## 568 569 570 571 572 573 574 575 576 577
## 11 7 3 7 9 9 6 15 8 8
## 578 579 580 581 582 583 584 585 586 587
## 7 14 17 11 9 4 7 9 8 11
## 588 589 590 591 592 593 594 596 597 598
## 8 10 18 7 9 16 13 8 6 7
## 599 600 601 602 603 604 605 606 607 608
## 8 47 4 10 14 7 7 3 7 9
## 609 610 611 612 613 614 615 616 617 618
## 11 9 7 2 4 9 9 8 10 5
## 619 620 621 622 623 624 625 626 627 628
## 7 7 11 12 7 16 7 8 8 9
## 629 630 631 632 633 634 635 636 637 638
## 7 8 5 9 8 13 5 7 7 9
## 639 640 641 642 643 644 645 646 647 648
## 12 7 10 5 11 9 4 11 7 9
## 649 650 651 652 653 654 655 656 657 658
## 7 8 7 8 6 3 10 7 2 4
## 659 660 661 662 663 664 665 666 667 668
## 6 13 8 8 8 4 8 9 8 9
## 669 670 671 672 673 674 675 676 677 678
## 12 5 15 4 9 6 12 8 5 8
## 679 680 681 682 683 684 685 686 687 688
## 5 7 2 7 5 12 8 10 3 12
## 689 690 691 692 693 694 695 696 697 698
## 7 9 7 5 7 3 5 8 5 10
## 699 700 701 702 703 704 705 706 707 708
## 8 34 8 6 5 4 6 6 7 6
## 709 710 711 712 713 714 715 716 717 718
## 9 9 6 7 8 7 7 3 8 6
## 719 720 721 722 723 724 725 726 727 728
## 4 7 4 7 7 8 11 4 7 9
## 729 730 731 732 733 734 735 736 737 738
## 4 7 7 5 9 9 9 6 6 2
## 739 740 741 742 743 744 745 746 748 749
## 5 6 12 5 8 7 8 7 8 7
## 750 751 752 753 754 755 756 757 758 759
## 47 7 7 4 4 12 8 8 10 8
## 760 761 762 763 764 765 766 767 768 769
## 6 4 10 12 5 7 10 10 5 9
## 770 771 772 773 774 775 776 777 778 779
## 6 5 4 8 9 7 6 11 5 7
## 780 781 782 783 784 785 786 787 788 789
## 4 6 1 6 8 7 3 7 9 6
## 790 791 792 793 794 795 796 797 798 799
## 7 6 8 5 9 8 5 7 5 4
## 800 801 802 803 804 805 806 807 808 809
## 57 5 7 5 3 3 7 5 10 5
## 810 811 812 813 814 815 816 817 818 819
## 4 5 2 3 8 3 5 7 6 5
## 820 821 822 823 824 825 826 827 828 829
## 5 9 7 4 8 7 7 4 8 1
## 830 831 832 833 834 835 836 837 838 839
## 4 6 7 4 7 5 3 6 11 4
## 840 841 842 843 844 845 846 847 848 849
## 6 2 3 2 3 5 10 2 7 6
## 850 851 852 853 854 855 856 857 858 859
## 7 5 7 6 8 4 7 8 4 8
## 860 861 862 863 864 866 867 868 869 870
## 7 5 11 9 11 11 7 5 7 8
## 871 872 873 874 875 876 877 878 879 880
## 8 8 12 6 7 5 6 8 4 5
## 881 882 883 884 885 886 887 888 889 890
## 8 5 4 3 5 5 4 4 1 5
## 891 892 893 894 895 896 897 898 899 900
## 11 4 6 8 5 3 5 2 6 25
## 901 902 903 904 905 906 907 908 909 910
## 6 8 8 6 7 5 8 6 11 12
## 911 912 913 914 915 916 917 918 919 920
## 6 7 2 6 4 11 4 8 6 9
## 921 922 923 924 925 926 927 928 929 930
## 8 9 3 6 4 9 4 7 2 3
## 931 932 933 934 935 936 937 938 939 940
## 5 7 5 5 4 9 2 3 2 11
## 941 942 943 944 945 946 947 948 949 950
## 6 6 4 6 9 4 4 11 3 8
## 951 952 953 954 955 956 957 958 959 960
## 3 1 3 3 9 10 4 5 2 8
## 961 962 963 964 965 966 967 968 969 970
## 10 6 4 4 1 7 14 3 9 3
## 971 972 973 974 975 976 977 978 979 980
## 7 1 5 4 9 3 5 2 6 8
## 981 982 983 984 985 986 987 988 989 990
## 7 6 7 10 10 6 2 6 5 5
## 991 992 993 994 995 996 997 998 999 1000
## 3 4 4 2 5 5 3 6 8 154
## 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010
## 6 3 5 3 6 9 6 8 10 8
## 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020
## 7 3 5 4 7 4 5 12 3 5
## 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030
## 7 4 7 10 4 9 9 3 6 4
## 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 2 4 6 6 9 3 5 7 5 4
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050
## 9 8 6 7 3 9 5 3 8 11
## 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060
## 7 5 4 8 6 5 4 4 2 4
## 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070
## 3 6 4 6 10 9 6 7 2 5
## 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080
## 6 2 4 6 5 4 3 8 4 5
## 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090
## 2 6 5 4 5 7 4 2 2 7
## 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100
## 4 2 10 3 4 3 5 5 8 17
## 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
## 8 10 6 1 7 3 5 5 2 3
## 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 7 4 4 4 6 7 2 8 3 2
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## 9651 9653 9654 9656 9657 9660 9663 9664 9665 9667
## 3 2 1 2 1 1 1 1 1 2
## 9669 9670 9671 9672 9673 9675 9676 9677 9678 9680
## 1 4 3 1 2 2 1 2 3 1
## 9681 9682 9683 9684 9685 9686 9687 9688 9689 9690
## 1 1 1 2 2 3 1 2 3 3
## 9694 9695 9697 9698 9700 9701 9704 9705 9708 9712
## 1 2 1 1 2 1 1 1 3 1
## 9713 9714 9715 9716 9717 9718 9719 9720 9722 9723
## 2 1 2 1 1 2 2 2 2 2
## 9725 9726 9728 9729 9730 9731 9733 9734 9735 9736
## 2 4 3 1 1 1 3 1 2 2
## 9737 9739 9740 9741 9743 9744 9747 9748 9749 9751
## 2 1 2 2 1 2 2 2 1 3
## 9752 9753 9755 9756 9758 9759 9762 9763 9764 9765
## 1 1 1 1 1 6 1 3 2 1
## 9766 9767 9768 9769 9770 9771 9772 9774 9775 9776
## 3 2 3 2 1 2 1 1 3 1
## 9777 9778 9779 9780 9782 9784 9786 9787 9788 9791
## 4 1 1 1 1 1 2 3 3 1
## 9792 9794 9795 9800 9803 9805 9807 9808 9810 9811
## 1 1 1 1 3 1 1 2 3 3
## 9812 9813 9814 9816 9818 9819 9820 9821 9822 9823
## 2 2 1 2 4 2 1 2 1 1
## 9824 9826 9829 9830 9832 9833 9834 9835 9837 9838
## 3 1 1 1 1 3 2 3 2 2
## 9839 9843 9845 9847 9848 9849 9850 9852 9853 9854
## 1 1 3 1 2 1 2 1 1 1
## 9855 9857 9858 9859 9860 9861 9862 9864 9865 9866
## 1 2 2 1 1 1 2 1 2 1
## 9867 9868 9869 9870 9871 9872 9873 9874 9875 9878
## 2 1 1 1 1 1 1 3 1 1
## 9879 9880 9882 9885 9887 9888 9890 9891 9893 9894
## 1 3 1 2 1 1 5 3 3 1
## 9895 9897 9898 9899 9900 9901 9902 9903 9904 9906
## 3 2 2 2 3 3 1 3 2 1
## 9908 9909 9911 9914 9915 9919 9920 9921 9922 9923
## 2 1 2 1 2 2 2 2 2 1
## 9925 9926 9927 9928 9929 9930 9933 9934 9935 9936
## 1 2 3 2 2 2 2 2 3 1
## 9939 9940 9941 9942 9944 9945 9947 9949 9950 9951
## 3 1 1 2 4 1 1 1 3 1
## 9954 9956 9958 9959 9961 9963 9964 9965 9967 9968
## 2 3 2 1 2 1 2 1 1 2
## 9969 9970 9971 9972 9975 9977 9979 9980 9981 9982
## 2 1 4 2 3 1 1 2 2 1
## 9984 9985 9987 9988 9990 9991 9993 9994 9996 9997
## 2 1 1 2 5 1 2 2 2 4
## 9998 9999 10000 10001 10002 10004 10005 10006 10008 10010
## 2 1 62 2 2 1 1 1 1 5
## 10011 10012 10014 10015 10017 10019 10020 10021 10022 10024
## 4 2 1 2 4 1 2 2 2 2
## 10025 10027 10030 10032 10034 10035 10036 10039 10040 10041
## 1 1 1 3 1 2 1 1 1 1
## 10043 10044 10045 10049 10050 10052 10054 10055 10057 10058
## 2 2 2 1 1 1 1 1 3 1
## 10059 10060 10061 10062 10064 10066 10067 10068 10070 10071
## 3 2 2 2 2 1 1 1 1 2
## 10074 10075 10076 10077 10079 10081 10082 10083 10085 10086
## 2 2 1 1 1 4 1 1 3 2
## 10087 10088 10089 10094 10095 10097 10099 10101 10102 10103
## 2 1 1 1 1 2 1 1 1 1
## 10105 10106 10109 10110 10111 10113 10114 10115 10118 10120
## 1 1 2 1 1 1 3 2 1 1
## 10121 10122 10124 10125 10126 10129 10130 10131 10133 10136
## 1 1 1 2 3 1 2 1 2 4
## 10137 10139 10144 10146 10148 10150 10151 10152 10153 10154
## 1 1 2 1 1 1 2 1 2 1
## 10155 10156 10157 10158 10159 10160 10163 10164 10165 10166
## 4 1 1 1 1 1 2 2 2 1
## 10167 10168 10169 10170 10173 10174 10177 10178 10179 10180
## 4 1 1 1 1 1 2 2 1 1
## 10181 10182 10184 10185 10186 10189 10191 10192 10194 10195
## 2 1 1 2 1 1 1 1 1 1
## 10196 10197 10198 10200 10201 10202 10204 10205 10206 10208
## 1 4 2 2 1 2 1 1 1 1
## 10209 10210 10211 10213 10214 10215 10216 10217 10219 10220
## 2 2 1 1 1 2 6 3 3 2
## 10221 10222 10223 10224 10225 10226 10229 10230 10231 10241
## 1 1 1 1 1 3 2 2 2 2
## 10242 10243 10244 10245 10246 10247 10248 10249 10250 10251
## 1 1 2 2 1 1 1 1 1 1
## 10253 10255 10257 10259 10260 10261 10263 10264 10265 10266
## 2 1 1 1 2 1 1 1 3 1
## 10270 10271 10273 10274 10275 10277 10278 10279 10280 10281
## 2 1 2 2 2 1 1 3 1 1
## 10283 10284 10285 10289 10291 10292 10293 10294 10296 10297
## 1 1 1 2 1 5 2 2 2 4
## 10298 10299 10300 10301 10302 10303 10304 10306 10307 10308
## 1 2 5 1 2 1 1 1 1 1
## 10310 10312 10313 10314 10316 10317 10319 10321 10322 10324
## 1 1 2 2 1 1 1 1 3 1
## 10327 10328 10329 10330 10331 10334 10336 10338 10340 10342
## 2 1 3 1 2 1 1 3 3 1
## 10343 10344 10346 10347 10349 10350 10352 10353 10354 10355
## 1 1 1 1 1 1 1 1 2 1
## 10356 10357 10358 10362 10363 10364 10366 10367 10368 10369
## 2 1 1 2 2 1 1 2 2 1
## 10370 10371 10373 10374 10375 10377 10378 10380 10382 10383
## 1 2 1 2 3 1 2 1 3 2
## 10386 10387 10388 10389 10390 10393 10394 10395 10396 10397
## 1 2 1 1 2 1 2 1 1 2
## 10398 10403 10404 10405 10407 10408 10410 10412 10413 10414
## 1 3 3 2 3 3 2 1 1 1
## 10415 10416 10419 10420 10421 10423 10424 10426 10427 10428
## 1 3 2 1 2 1 1 1 2 1
## 10429 10432 10433 10434 10436 10437 10438 10440 10441 10442
## 2 1 3 2 1 1 1 1 1 1
## 10443 10444 10445 10446 10447 10448 10450 10451 10452 10453
## 1 2 1 6 2 1 1 4 1 1
## 10454 10455 10458 10460 10461 10464 10465 10466 10467 10469
## 1 2 1 1 2 2 4 2 1 3
## 10471 10472 10474 10475 10476 10477 10480 10481 10484 10486
## 2 1 1 1 2 1 3 4 1 2
## 10487 10490 10491 10494 10495 10496 10497 10500 10501 10502
## 1 1 1 1 1 1 3 7 1 3
## 10504 10506 10507 10511 10512 10513 10516 10517 10521 10522
## 1 1 1 2 3 3 1 2 2 1
## 10523 10524 10529 10531 10532 10533 10534 10536 10537 10543
## 3 4 1 2 1 1 1 2 4 1
## 10544 10545 10547 10548 10549 10550 10551 10552 10553 10555
## 1 1 1 2 3 1 2 2 2 4
## 10556 10557 10558 10559 10560 10561 10562 10563 10564 10565
## 3 1 1 2 1 4 2 1 1 1
## 10568 10570 10571 10572 10573 10574 10575 10577 10582 10585
## 1 3 3 1 1 1 2 2 1 2
## 10586 10587 10588 10589 10590 10593 10594 10595 10596 10597
## 1 1 2 1 2 2 1 2 1 2
## 10598 10600 10603 10604 10605 10606 10609 10610 10614 10616
## 1 3 1 4 2 2 3 1 2 1
## 10619 10620 10624 10625 10626 10627 10628 10630 10631 10633
## 1 2 1 1 1 1 2 1 1 1
## 10634 10635 10636 10637 10638 10639 10640 10641 10643 10648
## 2 4 3 2 2 3 1 2 1 1
## 10649 10650 10651 10653 10654 10655 10656 10657 10658 10659
## 1 3 1 1 2 1 4 1 2 3
## 10660 10661 10666 10667 10668 10676 10677 10678 10679 10682
## 2 1 2 6 1 1 1 1 1 1
## 10683 10684 10685 10686 10687 10688 10689 10691 10693 10695
## 1 1 1 1 2 1 1 1 2 1
## 10696 10697 10698 10700 10704 10706 10707 10708 10712 10713
## 1 1 4 1 1 3 1 3 2 1
## 10714 10717 10719 10720 10721 10722 10724 10725 10726 10727
## 1 3 1 3 1 2 1 1 1 1
## 10729 10730 10731 10732 10733 10734 10735 10738 10739 10740
## 1 1 2 1 2 3 3 1 2 3
## 10741 10744 10746 10747 10749 10750 10751 10753 10755 10756
## 1 1 1 1 1 1 1 1 3 1
## 10758 10759 10760 10761 10766 10767 10768 10769 10770 10771
## 2 1 1 3 1 1 1 1 1 1
## 10772 10774 10775 10778 10782 10783 10785 10786 10787 10789
## 1 1 2 2 3 2 2 1 2 1
## 10791 10792 10793 10794 10796 10797 10802 10803 10805 10809
## 1 1 1 1 2 1 1 1 2 2
## 10810 10813 10814 10815 10817 10818 10820 10821 10822 10823
## 1 1 2 1 1 2 1 3 1 2
## 10824 10825 10826 10828 10830 10832 10834 10837 10840 10841
## 1 3 2 3 1 2 1 1 2 2
## 10843 10845 10847 10848 10850 10851 10852 10853 10856 10857
## 1 2 2 1 2 1 1 1 1 1
## 10858 10859 10860 10861 10862 10864 10865 10867 10869 10870
## 2 3 2 3 1 1 2 1 1 1
## 10872 10873 10874 10875 10876 10878 10880 10881 10883 10884
## 1 1 1 4 2 1 1 1 3 1
## 10886 10887 10889 10892 10893 10894 10895 10896 10898 10900
## 2 1 1 1 3 1 1 1 1 2
## 10901 10902 10903 10908 10909 10910 10911 10912 10914 10915
## 1 1 1 1 1 3 1 1 1 1
## 10916 10918 10919 10920 10923 10924 10925 10926 10928 10929
## 1 2 1 2 1 2 3 1 4 2
## 10930 10931 10932 10933 10934 10936 10938 10940 10942 10944
## 2 4 1 1 1 1 1 2 1 2
## 10945 10946 10947 10951 10954 10955 10956 10957 10958 10959
## 1 2 2 1 3 2 1 1 3 1
## 10960 10962 10964 10965 10968 10969 10970 10971 10972 10975
## 1 1 2 1 1 2 2 1 1 1
## 10979 10980 10981 10982 10983 10984 10985 10989 10990 10991
## 3 3 2 1 3 1 1 2 1 5
## 10993 10994 10995 10996 10999 11000 11001 11002 11003 11009
## 1 2 1 1 2 10 2 1 1 1
## 11010 11014 11020 11024 11025 11027 11028 11030 11031 11032
## 1 2 2 1 2 1 1 1 1 2
## 11035 11036 11037 11041 11042 11043 11044 11045 11047 11049
## 1 3 1 1 1 3 1 1 1 1
## 11050 11051 11052 11053 11054 11055 11056 11057 11058 11062
## 1 2 2 3 1 1 1 2 2 1
## 11064 11067 11071 11073 11076 11077 11079 11081 11082 11083
## 2 1 1 1 2 2 1 2 4 3
## 11084 11087 11088 11089 11090 11092 11093 11094 11095 11096
## 2 1 1 1 1 2 1 5 2 2
## 11099 11100 11101 11102 11104 11108 11111 11113 11114 11116
## 1 1 3 1 4 1 2 1 2 3
## 11117 11118 11121 11123 11124 11125 11126 11129 11130 11135
## 1 1 1 1 4 1 1 2 1 1
## 11136 11137 11138 11139 11140 11141 11146 11147 11149 11151
## 1 2 2 1 2 1 1 1 1 2
## 11154 11157 11160 11161 11163 11165 11168 11169 11171 11172
## 2 3 1 1 3 1 2 2 1 1
## 11173 11176 11177 11178 11179 11183 11184 11185 11186 11187
## 2 1 2 2 1 2 1 2 1 2
## 11189 11192 11193 11194 11198 11199 11200 11201 11208 11209
## 1 1 1 2 2 2 5 3 1 7
## 11210 11215 11217 11220 11222 11223 11224 11226 11229 11232
## 1 3 1 1 2 2 1 1 3 2
## 11234 11235 11236 11238 11239 11241 11244 11245 11247 11248
## 3 2 1 2 2 4 1 2 4 1
## 11250 11253 11254 11255 11256 11257 11258 11259 11260 11261
## 1 2 1 1 1 1 2 2 1 2
## 11262 11264 11265 11266 11267 11268 11269 11271 11273 11274
## 1 2 1 1 6 1 1 1 2 2
## 11275 11276 11277 11278 11280 11281 11282 11283 11289 11290
## 2 2 3 1 1 2 1 2 2 3
## 11292 11293 11294 11295 11297 11298 11299 11300 11302 11303
## 1 1 1 1 3 3 2 2 3 1
## 11304 11305 11306 11307 11308 11312 11314 11315 11316 11317
## 2 1 1 1 3 1 1 1 2 2
## 11318 11319 11320 11321 11324 11329 11332 11333 11334 11336
## 1 1 2 2 1 1 1 2 2 1
## 11339 11341 11343 11345 11346 11347 11350 11351 11353 11355
## 2 2 1 3 3 1 3 2 1 2
## 11356 11360 11361 11364 11368 11369 11370 11371 11373 11374
## 1 1 3 2 1 1 1 2 1 1
## 11376 11377 11378 11379 11383 11385 11387 11391 11392 11395
## 1 1 2 1 2 1 1 1 1 2
## 11398 11401 11403 11404 11405 11407 11409 11412 11413 11416
## 2 1 1 1 2 2 1 1 1 1
## 11417 11418 11419 11421 11426 11427 11430 11431 11432 11434
## 1 1 1 1 1 1 2 1 1 1
## 11435 11436 11437 11445 11447 11448 11449 11450 11451 11452
## 4 1 2 1 1 1 1 1 1 1
## 11453 11454 11457 11458 11462 11463 11464 11465 11466 11467
## 1 1 1 1 1 1 1 1 1 1
## 11468 11470 11471 11472 11473 11474 11475 11479 11480 11481
## 3 2 2 1 1 2 2 1 1 1
## 11482 11486 11488 11489 11490 11491 11492 11493 11495 11496
## 2 3 1 2 1 1 2 2 3 1
## 11500 11501 11504 11505 11506 11508 11509 11510 11511 11512
## 6 1 1 1 3 1 1 2 2 1
## 11514 11515 11516 11517 11518 11521 11523 11525 11526 11529
## 2 1 1 1 1 3 2 1 1 2
## 11530 11531 11536 11539 11540 11541 11542 11544 11546 11547
## 3 1 2 1 1 1 2 1 1 1
## 11549 11552 11553 11554 11556 11557 11558 11559 11562 11565
## 1 1 2 1 1 1 2 1 2 1
## 11566 11567 11568 11569 11572 11573 11574 11575 11577 11579
## 1 1 6 2 1 1 1 2 1 1
## 11581 11582 11583 11584 11585 11586 11588 11590 11591 11592
## 2 1 1 1 2 3 3 2 2 1
## 11593 11594 11595 11597 11600 11601 11603 11604 11607 11608
## 1 1 1 1 5 1 2 2 1 1
## 11610 11612 11613 11615 11618 11619 11620 11623 11624 11625
## 2 1 1 1 1 3 1 1 1 1
## 11626 11628 11630 11634 11635 11636 11637 11638 11639 11640
## 1 3 1 1 1 2 1 1 1 2
## 11642 11643 11644 11645 11646 11647 11648 11649 11650 11651
## 2 2 3 1 1 2 1 3 1 1
## 11652 11653 11655 11656 11657 11658 11661 11662 11663 11664
## 1 1 3 2 3 1 1 4 2 1
## 11665 11666 11667 11668 11669 11671 11677 11678 11679 11680
## 4 1 1 1 3 1 1 1 1 1
## 11681 11683 11687 11689 11691 11692 11693 11695 11696 11699
## 2 1 1 1 2 1 1 1 1 2
## 11700 11701 11703 11706 11708 11709 11710 11713 11714 11723
## 1 3 2 2 4 3 3 2 1 3
## 11724 11725 11726 11729 11731 11732 11733 11735 11736 11737
## 1 1 2 1 1 1 1 1 1 1
## 11740 11741 11742 11744 11745 11746 11747 11748 11751 11752
## 1 1 1 1 1 1 2 1 3 1
## 11753 11754 11755 11757 11758 11759 11760 11761 11762 11763
## 2 1 1 1 1 1 1 1 2 2
## 11766 11767 11768 11769 11770 11771 11773 11774 11775 11779
## 2 1 3 1 1 1 1 3 2 1
## 11780 11782 11783 11790 11791 11794 11795 11796 11799 11801
## 1 1 1 2 1 1 1 1 2 1
## 11804 11805 11808 11812 11813 11814 11815 11816 11818 11819
## 1 1 2 2 1 2 1 2 1 1
## 11820 11821 11822 11823 11826 11829 11830 11831 11833 11834
## 3 1 1 2 3 2 3 2 1 1
## 11835 11836 11837 11838 11839 11840 11841 11842 11844 11846
## 1 1 1 2 1 2 2 1 1 1
## 11848 11849 11850 11851 11854 11856 11857 11859 11861 11862
## 2 3 1 2 2 1 1 1 1 2
## 11863 11864 11865 11866 11867 11868 11869 11870 11871 11873
## 1 3 1 1 1 1 2 2 1 1
## 11874 11876 11877 11879 11880 11881 11883 11885 11886 11887
## 1 2 4 2 1 2 2 1 1 2
## 11888 11889 11891 11892 11894 11896 11900 11901 11902 11903
## 2 2 3 1 2 3 2 1 3 1
## 11905 11906 11907 11909 11910 11914 11916 11917 11918 11920
## 1 2 1 1 2 1 1 2 2 2
## 11922 11923 11929 11930 11932 11933 11934 11936 11937 11938
## 2 1 1 2 1 1 1 1 1 2
## 11939 11940 11942 11943 11944 11945 11946 11947 11948 11949
## 2 1 2 1 1 2 1 1 1 1
## 11950 11951 11952 11955 11956 11957 11959 11962 11963 11965
## 2 2 1 1 1 2 4 1 1 1
## 11966 11967 11971 11972 11973 11977 11978 11979 11980 11981
## 1 1 1 5 4 2 1 1 1 3
## 11982 11984 11986 11987 11988 11990 11993 11994 11995 12000
## 1 2 3 2 1 1 1 1 2 25
## 12003 12005 12007 12009 12010 12011 12013 12016 12017 12019
## 1 1 3 2 1 1 1 2 2 2
## 12021 12023 12025 12026 12027 12031 12032 12034 12035 12036
## 1 2 3 1 1 3 1 1 2 1
## 12039 12040 12041 12042 12047 12049 12050 12051 12052 12053
## 2 1 1 1 1 1 1 2 1 2
## 12055 12056 12058 12059 12060 12062 12064 12065 12066 12072
## 2 2 1 2 1 1 1 1 1 2
## 12073 12081 12082 12083 12084 12085 12089 12090 12092 12094
## 1 1 1 1 1 2 1 1 2 2
## 12095 12099 12100 12101 12105 12106 12108 12109 12110 12113
## 1 3 2 1 3 1 1 1 2 3
## 12114 12115 12117 12118 12119 12120 12121 12125 12126 12127
## 1 1 1 1 2 2 1 1 3 2
## 12129 12131 12132 12133 12134 12135 12142 12145 12146 12147
## 1 2 2 2 2 1 3 1 2 1
## 12149 12150 12151 12153 12155 12156 12157 12158 12159 12161
## 1 1 4 1 1 3 1 1 1 1
## 12162 12164 12165 12168 12169 12170 12171 12174 12175 12176
## 3 1 1 2 1 1 2 3 2 1
## 12177 12178 12180 12181 12186 12187 12189 12190 12191 12192
## 3 2 2 1 3 1 1 3 2 1
## 12193 12194 12195 12197 12198 12199 12200 12201 12202 12203
## 2 1 1 1 3 1 2 2 1 2
## 12204 12206 12207 12208 12210 12212 12213 12215 12216 12217
## 2 2 2 1 1 4 3 1 3 1
## 12219 12220 12221 12222 12223 12224 12225 12226 12227 12228
## 3 2 1 1 1 3 1 1 1 2
## 12229 12230 12231 12232 12237 12238 12239 12240 12241 12242
## 1 1 3 1 3 1 1 2 2 1
## 12243 12244 12246 12251 12253 12256 12259 12263 12264 12266
## 2 1 2 1 1 3 1 2 2 1
## 12268 12269 12271 12277 12278 12279 12282 12284 12285 12288
## 3 2 1 2 1 1 3 3 1 1
## 12289 12290 12293 12295 12297 12298 12299 12300 12301 12302
## 2 2 1 1 1 2 1 3 2 1
## 12303 12304 12305 12310 12311 12313 12315 12316 12317 12318
## 2 1 3 1 1 1 1 2 1 1
## 12320 12321 12324 12325 12327 12331 12332 12333 12334 12336
## 2 2 3 1 1 1 3 1 1 1
## 12337 12338 12339 12340 12342 12344 12347 12349 12350 12351
## 1 2 3 1 1 1 1 1 1 2
## 12353 12354 12356 12357 12358 12366 12367 12368 12370 12372
## 1 2 2 1 1 1 3 2 1 2
## 12373 12375 12376 12378 12379 12380 12381 12382 12383 12384
## 2 1 1 1 1 1 1 1 3 1
## 12386 12387 12388 12389 12392 12395 12397 12399 12402 12403
## 2 1 1 1 1 1 1 1 2 2
## 12404 12405 12407 12409 12410 12411 12412 12413 12414 12415
## 1 1 3 2 2 1 1 2 1 1
## 12416 12418 12419 12420 12421 12422 12424 12427 12428 12429
## 1 2 2 1 1 2 3 1 1 1
## 12431 12432 12433 12434 12435 12443 12446 12447 12448 12449
## 2 2 2 1 3 3 1 1 1 2
## 12450 12451 12453 12454 12455 12459 12461 12463 12464 12466
## 1 1 2 1 1 2 3 2 1 2
## 12468 12469 12470 12471 12475 12483 12484 12486 12488 12489
## 1 1 2 1 1 1 1 1 1 2
## 12490 12492 12493 12494 12496 12498 12499 12500 12503 12505
## 1 2 3 2 2 1 1 10 1 2
## 12509 12511 12512 12514 12515 12518 12520 12523 12524 12526
## 1 1 1 3 1 1 2 2 1 2
## 12528 12531 12532 12533 12537 12541 12543 12544 12546 12548
## 1 3 1 2 1 1 1 1 2 2
## 12549 12555 12556 12557 12559 12563 12564 12565 12566 12568
## 2 1 1 2 1 2 1 2 1 1
## 12569 12570 12571 12574 12578 12580 12581 12582 12585 12586
## 1 1 2 2 1 1 3 2 1 3
## 12587 12588 12589 12590 12592 12593 12595 12596 12598 12600
## 1 2 2 4 1 2 1 2 1 4
## 12603 12604 12606 12608 12609 12610 12612 12614 12620 12621
## 1 1 2 1 3 1 2 1 1 1
## 12622 12624 12627 12628 12631 12632 12636 12637 12639 12640
## 2 2 1 1 3 2 1 1 1 2
## 12641 12642 12648 12649 12650 12653 12656 12657 12659 12661
## 3 1 1 1 1 3 1 1 1 1
## 12662 12663 12665 12667 12668 12669 12670 12671 12673 12675
## 1 2 1 1 2 2 1 1 1 2
## 12678 12679 12682 12683 12684 12685 12686 12687 12688 12695
## 1 1 1 1 1 3 1 1 2 1
## 12696 12699 12700 12701 12702 12706 12707 12709 12711 12712
## 1 1 1 1 1 1 1 1 1 2
## 12713 12716 12717 12718 12720 12721 12722 12724 12725 12726
## 2 1 2 2 1 1 3 1 1 1
## 12727 12728 12730 12732 12733 12734 12736 12740 12743 12744
## 2 2 1 3 2 3 1 2 1 2
## 12745 12748 12751 12754 12756 12757 12759 12763 12764 12766
## 2 2 2 2 1 2 2 1 1 1
## 12767 12769 12770 12773 12775 12778 12779 12780 12781 12782
## 3 1 1 1 3 2 1 2 1 1
## 12788 12789 12790 12791 12792 12793 12795 12796 12797 12798
## 1 2 2 2 1 2 1 2 1 1
## 12800 12807 12808 12810 12812 12813 12814 12815 12819 12820
## 2 3 1 2 1 1 1 1 1 2
## 12821 12823 12826 12829 12830 12831 12833 12834 12835 12836
## 3 2 1 3 2 1 2 2 1 3
## 12837 12838 12839 12842 12845 12846 12848 12849 12850 12853
## 2 2 1 1 1 1 1 1 2 1
## 12855 12856 12859 12862 12864 12866 12868 12869 12870 12871
## 2 2 1 1 1 4 1 2 1 2
## 12875 12877 12879 12880 12881 12882 12883 12884 12886 12887
## 2 2 1 1 1 1 1 1 2 3
## 12888 12891 12894 12895 12896 12899 12900 12901 12903 12904
## 1 1 3 1 1 1 1 2 1 2
## 12905 12906 12907 12908 12909 12910 12911 12912 12913 12914
## 1 1 1 1 2 1 2 2 1 1
## 12918 12919 12920 12921 12922 12924 12926 12927 12928 12931
## 1 1 2 2 1 2 3 1 1 1
## 12932 12934 12935 12936 12937 12938 12941 12942 12946 12947
## 1 2 1 3 2 2 2 1 1 2
## 12948 12949 12956 12957 12958 12960 12962 12963 12965 12968
## 2 1 2 1 2 2 1 1 1 1
## 12970 12971 12972 12973 12974 12976 12980 12981 12983 12984
## 1 1 1 1 1 2 1 1 2 2
## 12989 12990 12991 12993 12995 12996 12997 12998 12999 13000
## 1 1 1 2 2 3 2 5 2 12
## 13003 13004 13005 13006 13007 13008 13010 13013 13014 13015
## 1 2 1 1 1 2 1 2 2 2
## 13017 13018 13019 13021 13022 13023 13024 13026 13027 13028
## 2 1 1 1 6 2 1 1 3 3
## 13029 13030 13031 13034 13035 13036 13039 13040 13041 13042
## 1 1 1 1 1 2 2 1 1 1
## 13044 13046 13049 13050 13055 13056 13061 13062 13066 13067
## 5 1 1 1 1 1 1 2 1 2
## 13071 13072 13073 13075 13078 13080 13081 13082 13083 13084
## 1 1 1 2 3 1 1 1 1 2
## 13086 13087 13088 13090 13093 13095 13096 13097 13098 13099
## 1 2 1 5 2 1 1 1 2 3
## 13100 13101 13102 13104 13108 13109 13110 13111 13112 13117
## 1 3 1 3 1 2 1 1 2 2
## 13118 13119 13120 13121 13122 13123 13124 13125 13126 13129
## 1 2 2 1 1 1 1 1 1 1
## 13130 13131 13132 13134 13135 13137 13138 13139 13141 13142
## 3 1 1 3 1 1 2 2 1 2
## 13143 13146 13147 13149 13150 13152 13155 13157 13159 13160
## 1 5 1 3 3 1 1 1 2 2
## 13161 13167 13169 13171 13173 13174 13175 13177 13178 13180
## 2 1 3 1 1 1 1 2 2 4
## 13181 13182 13183 13184 13185 13186 13189 13190 13191 13193
## 2 1 1 1 2 1 1 2 2 2
## 13194 13198 13199 13200 13201 13205 13206 13210 13214 13219
## 1 1 1 3 3 1 2 1 1 1
## 13220 13221 13222 13223 13232 13233 13235 13236 13237 13239
## 2 1 2 1 1 2 1 1 3 2
## 13242 13243 13245 13246 13247 13249 13253 13254 13258 13260
## 1 2 1 1 1 2 1 1 2 1
## 13261 13263 13265 13267 13268 13269 13271 13272 13275 13276
## 1 1 3 2 1 3 2 1 1 1
## 13279 13280 13281 13283 13284 13285 13286 13288 13289 13291
## 1 1 1 2 2 1 3 2 1 3
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## 22259 22261 22264 22275 22277 22278 22282 22285 22286 22290
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## 22292 22299 22300 22302 22303 22304 22305 22306 22307 22311
## 1 1 1 1 1 2 1 1 1 1
## 22318 22319 22321 22324 22326 22327 22328 22330 22338 22340
## 1 2 1 1 1 1 2 1 1 2
## 22342 22344 22347 22351 22352 22356 22358 22360 22361 22362
## 1 1 1 2 1 1 1 1 1 1
## 22363 22364 22367 22368 22369 22370 22371 22373 22376 22377
## 1 1 1 1 1 2 1 1 1 1
## 22378 22380 22381 22382 22383 22387 22388 22389 22390 22391
## 1 1 1 1 3 1 1 3 2 3
## 22392 22394 22396 22398 22399 22403 22404 22405 22408 22415
## 2 1 3 1 1 1 1 2 1 1
## 22416 22423 22424 22427 22428 22430 22431 22434 22438 22441
## 1 2 1 2 1 1 1 3 1 6
## 22442 22445 22447 22448 22449 22450 22451 22452 22453 22457
## 1 2 1 1 1 1 1 1 1 1
## 22459 22460 22461 22462 22463 22465 22466 22468 22469 22470
## 1 1 1 1 2 1 1 1 2 1
## 22472 22473 22474 22476 22478 22480 22481 22482 22484 22485
## 1 2 2 3 2 1 1 3 1 3
## 22488 22489 22490 22496 22498 22500 22502 22505 22506 22508
## 1 2 1 1 3 1 2 2 1 1
## 22514 22515 22516 22517 22518 22519 22520 22521 22522 22527
## 1 3 1 1 1 3 2 1 1 1
## 22530 22533 22538 22540 22541 22542 22543 22545 22547 22548
## 3 2 4 1 1 1 1 2 1 1
## 22550 22551 22553 22557 22558 22559 22561 22563 22564 22566
## 1 1 1 1 1 1 2 1 1 1
## 22567 22569 22570 22571 22573 22574 22578 22579 22582 22583
## 2 1 1 1 1 1 1 1 2 1
## 22585 22586 22587 22588 22592 22595 22596 22598 22602 22606
## 1 1 1 1 2 1 1 1 1 2
## 22607 22608 22609 22611 22612 22614 22619 22620 22623 22626
## 2 1 1 2 1 2 1 1 1 2
## 22627 22630 22631 22633 22636 22640 22641 22643 22651 22652
## 1 2 1 2 1 1 3 1 2 1
## 22653 22655 22656 22659 22666 22668 22670 22671 22672 22683
## 2 1 2 3 1 1 1 1 1 1
## 22685 22687 22690 22691 22693 22696 22697 22699 22700 22701
## 1 1 1 2 1 1 1 1 1 1
## 22703 22704 22705 22708 22709 22713 22716 22718 22719 22720
## 1 1 1 1 3 1 1 3 1 1
## 22723 22724 22727 22729 22732 22734 22738 22740 22744 22749
## 2 1 1 2 1 1 1 1 1 1
## 22750 22752 22753 22755 22757 22758 22760 22764 22765 22769
## 1 2 1 1 1 3 1 1 1 1
## 22770 22771 22772 22773 22777 22782 22784 22785 22786 22787
## 2 2 1 1 1 1 1 2 1 1
## 22789 22790 22791 22792 22795 22796 22797 22798 22799 22800
## 3 2 2 1 1 3 1 3 1 3
## 22802 22805 22807 22815 22817 22818 22821 22822 22823 22824
## 1 1 1 2 1 1 2 1 2 1
## 22827 22828 22829 22830 22831 22832 22837 22838 22839 22841
## 1 1 1 4 1 2 1 1 2 1
## 22844 22846 22853 22855 22856 22857 22859 22867 22868 22869
## 2 1 2 1 2 2 1 1 1 2
## 22870 22871 22874 22875 22880 22881 22882 22883 22885 22889
## 2 1 1 2 1 2 1 1 1 1
## 22890 22891 22892 22893 22899 22900 22908 22909 22910 22914
## 1 2 2 1 3 3 1 1 1 1
## 22915 22916 22920 22922 22926 22927 22931 22934 22935 22936
## 1 1 2 2 1 1 1 1 1 1
## 22940 22942 22943 22945 22949 22950 22951 22955 22956 22959
## 2 1 1 2 1 1 1 1 2 1
## 22962 22963 22965 22966 22969 22972 22975 22976 22981 22982
## 3 1 1 1 2 1 1 1 1 1
## 22988 22990 22991 22992 22996 22998 22999 23000 23004 23006
## 1 2 1 1 1 3 1 2 1 2
## 23007 23009 23011 23012 23017 23019 23021 23023 23026 23028
## 1 1 1 1 1 1 1 1 1 2
## 23029 23031 23033 23034 23035 23036 23037 23038 23040 23047
## 1 1 1 1 2 1 1 1 1 1
## 23049 23050 23051 23052 23058 23064 23069 23070 23073 23078
## 1 1 1 1 2 1 1 1 1 1
## 23079 23080 23081 23083 23084 23087 23088 23091 23092 23093
## 1 2 1 1 1 1 1 2 1 1
## 23095 23096 23097 23100 23101 23108 23109 23110 23113 23114
## 1 1 2 2 2 2 2 1 3 1
## 23118 23120 23122 23124 23125 23130 23131 23133 23134 23135
## 1 1 3 1 1 1 1 3 1 2
## 23139 23141 23142 23145 23151 23153 23154 23156 23158 23161
## 1 1 1 1 1 1 1 2 2 1
## 23162 23163 23167 23168 23171 23172 23173 23174 23177 23178
## 1 1 2 1 1 1 1 1 1 1
## 23180 23181 23182 23184 23185 23186 23188 23190 23191 23192
## 2 1 1 1 1 3 1 1 1 1
## 23193 23194 23195 23200 23201 23203 23204 23207 23209 23210
## 1 1 1 1 1 1 1 1 3 1
## 23214 23215 23216 23217 23219 23220 23227 23230 23232 23234
## 1 2 1 1 3 3 1 1 1 1
## 23235 23236 23243 23244 23248 23249 23252 23254 23256 23257
## 3 1 1 2 1 1 1 2 1 2
## 23261 23263 23265 23266 23268 23273 23274 23276 23277 23278
## 3 1 1 2 1 1 1 2 1 1
## 23279 23281 23282 23283 23288 23290 23293 23295 23296 23298
## 1 3 3 1 1 1 1 2 1 2
## 23300 23301 23302 23306 23312 23315 23316 23317 23320 23324
## 1 1 1 1 1 1 1 2 1 1
## 23325 23327 23328 23329 23333 23334 23335 23337 23339 23341
## 1 1 1 1 5 1 1 1 1 1
## 23343 23346 23347 23348 23349 23354 23357 23361 23362 23363
## 3 1 1 1 1 2 2 2 1 2
## 23365 23370 23372 23373 23375 23378 23380 23385 23390 23393
## 2 1 2 2 1 1 6 1 2 2
## 23395 23397 23399 23400 23401 23403 23405 23411 23413 23416
## 1 2 1 1 4 3 1 1 1 1
## 23417 23419 23425 23426 23427 23429 23430 23433 23439 23440
## 1 1 1 1 1 1 1 2 1 1
## 23441 23443 23447 23452 23454 23455 23456 23458 23459 23461
## 1 1 1 1 1 1 1 1 1 1
## 23467 23468 23470 23471 23478 23479 23481 23482 23483 23484
## 1 1 1 1 1 1 2 1 1 1
## 23490 23492 23494 23496 23500 23506 23508 23509 23511 23514
## 1 1 1 1 3 2 1 1 1 1
## 23517 23519 23521 23526 23527 23530 23531 23537 23539 23542
## 1 1 3 2 2 2 1 1 1 2
## 23549 23554 23560 23561 23562 23563 23566 23567 23568 23570
## 1 1 1 2 3 1 1 2 1 1
## 23574 23577 23583 23585 23587 23588 23589 23590 23591 23596
## 2 1 1 1 1 1 1 1 1 1
## 23600 23607 23608 23609 23611 23616 23622 23625 23628 23629
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## 23630 23632 23633 23636 23637 23638 23644 23651 23655 23656
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## 23658 23659 23662 23663 23665 23667 23670 23672 23675 23678
## 1 2 1 1 1 1 1 1 1 1
## 23680 23682 23685 23686 23688 23690 23692 23693 23694 23695
## 1 1 1 1 1 1 3 1 3 1
## 23697 23699 23700 23705 23707 23709 23710 23711 23713 23715
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## 23716 23721 23727 23728 23729 23731 23732 23733 23736 23740
## 1 1 2 1 1 1 1 1 2 1
## 23741 23743 23744 23745 23746 23747 23748 23749 23751 23753
## 1 1 1 1 1 1 1 2 1 3
## 23758 23761 23764 23768 23770 23773 23774 23776 23777 23780
## 1 2 1 1 2 1 1 2 2 1
## 23781 23783 23784 23785 23786 23788 23789 23791 23793 23796
## 1 2 1 1 1 1 1 1 1 1
## 23798 23800 23801 23803 23804 23806 23809 23811 23813 23819
## 1 1 3 2 1 1 1 1 2 1
## 23824 23826 23827 23828 23829 23830 23832 23835 23839 23847
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## 23848 23853 23855 23856 23857 23859 23862 23867 23870 23872
## 2 2 2 1 1 1 1 2 1 1
## 23876 23878 23880 23881 23885 23886 23887 23888 23890 23892
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## 23893 23895 23900 23905 23912 23913 23919 23922 23936 23937
## 2 1 1 1 2 1 2 1 1 1
## 23938 23940 23944 23946 23947 23949 23953 23955 23956 23957
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## 23960 23961 23963 23966 23970 23971 23972 23973 23974 23981
## 2 1 1 2 2 2 1 1 1 1
## 23982 23983 23989 23991 23993 23994 23995 23999 24000 24001
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## 24003 24004 24007 24010 24012 24013 24014 24015 24017 24021
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## 24025 24026 24027 24028 24029 24033 24034 24039 24040 24049
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## 24050 24051 24053 24055 24058 24063 24066 24067 24068 24069
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## 24071 24075 24076 24078 24079 24082 24084 24085 24087 24093
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## 24096 24100 24102 24103 24105 24107 24108 24111 24113 24114
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## 1 1 1 1 3 3 2 1 3 1
## 24941 24942 24944 24948 24949 24950 24951 24952 24955 24956
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## 24999 25000 25002 25005 25007 25009 25011 25021 25027 25033
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## 25034 25037 25038 25040 25041 25045 25049 25052 25055 25056
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## 25058 25059 25061 25066 25068 25069 25074 25075 25077 25080
## 2 1 1 1 2 1 1 1 1 1
## 25081 25084 25085 25086 25092 25094 25098 25100 25105 25107
## 1 1 1 1 1 1 2 1 1 1
## 25108 25109 25111 25118 25119 25121 25124 25125 25126 25129
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## 25297 25298 25301 25303 25313 25314 25316 25318 25320 25327
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## 2 1 1 1 1 1 3 1 2 1
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## 2 1 1 1 1 1 1 1 1 1
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## 1 2 2 3 3 1 2 1 2 1
## 25659 25664 25668 25671 25672 25673 25674 25681 25682 25684
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## 25686 25687 25695 25697 25699 25700 25701 25702 25703 25712
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## 25713 25714 25717 25718 25719 25722 25724 25725 25729 25731
## 1 1 1 1 1 2 1 2 2 1
## 25732 25735 25736 25737 25740 25742 25743 25745 25746 25747
## 1 1 1 1 1 3 2 1 1 1
## 25750 25752 25753 25755 25756 25757 25760 25763 25767 25768
## 1 1 1 4 1 2 1 1 1 2
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## 1 1 2 1 1 1 2 1 1 1
## 25806 25809 25812 25814 25815 25818 25819 25820 25823 25826
## 1 1 1 1 1 2 1 2 1 1
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## 1 2 1 1 1 1 1 3 1 1
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## 1 1 1 2 1 1 1 2 1 1
## 25883 25885 25887 25888 25889 25890 25891 25894 25895 25898
## 1 1 1 1 1 1 1 1 1 1
## 25902 25905 25906 25910 25912 25915 25917 25918 25919 25920
## 1 2 1 1 1 1 1 2 2 1
## 25924 25926 25928 25929 25933 25936 25937 25940 25944 25945
## 1 2 1 1 1 1 2 1 1 1
## 25947 25951 25959 25960 25962 25963 25965 25967 25970 25971
## 4 1 1 1 1 1 1 1 1 1
## 25978 25979 25981 25982 25983 25984 25992 25999 26000 26001
## 2 2 1 1 2 1 1 1 5 1
## 26002 26006 26007 26008 26010 26013 26017 26018 26023 26025
## 1 2 1 1 1 4 1 1 1 1
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## 26078 26079 26085 26087 26088 26091 26094 26096 26099 26105
## 1 1 1 1 1 3 1 1 1 3
## 26110 26111 26112 26118 26121 26122 26125 26128 26130 26133
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## 26134 26136 26137 26138 26140 26142 26144 26146 26147 26149
## 1 1 1 2 1 1 2 2 1 1
## 26153 26159 26160 26165 26166 26167 26170 26172 26173 26175
## 1 1 1 2 1 1 1 1 2 2
## 26176 26179 26180 26181 26183 26185 26187 26189 26190 26191
## 1 1 1 1 1 2 1 1 1 1
## 26195 26200 26203 26208 26211 26212 26214 26215 26218 26223
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## 26225 26226 26227 26230 26233 26241 26253 26259 26260 26262
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## 26264 26265 26268 26269 26271 26273 26274 26275 26278 26279
## 1 1 1 1 1 1 2 2 1 1
## 26281 26282 26286 26288 26292 26294 26295 26296 26299 26300
## 2 1 1 1 1 2 1 1 1 1
## 26304 26306 26308 26310 26311 26312 26313 26318 26321 26327
## 1 1 1 1 1 2 1 2 1 1
## 26334 26337 26340 26341 26344 26349 26350 26353 26357 26358
## 1 1 1 1 2 1 1 1 1 1
## 26362 26366 26367 26370 26372 26373 26375 26379 26384 26389
## 1 1 1 1 1 2 2 1 1 1
## 26391 26396 26398 26405 26409 26410 26413 26422 26423 26425
## 1 1 1 1 1 1 1 1 1 1
## 26428 26429 26430 26431 26434 26435 26438 26440 26447 26451
## 2 2 1 1 2 1 1 1 1 3
## 26452 26457 26458 26459 26464 26465 26468 26469 26473 26476
## 1 1 1 1 1 2 1 1 2 1
## 26478 26479 26483 26484 26486 26487 26488 26489 26493 26494
## 1 1 1 1 1 1 1 3 2 2
## 26500 26505 26508 26512 26516 26517 26523 26524 26526 26529
## 1 1 1 1 1 1 1 1 2 1
## 26530 26532 26536 26537 26541 26542 26545 26548 26550 26551
## 1 1 1 1 1 2 1 1 1 2
## 26554 26555 26557 26562 26565 26568 26569 26570 26571 26572
## 1 1 1 2 1 1 1 3 1 1
## 26574 26576 26580 26584 26585 26588 26589 26596 26598 26599
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## 26600 26601 26603 26604 26606 26607 26608 26609 26614 26615
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## 26617 26621 26622 26624 26625 26626 26627 26628 26633 26635
## 1 2 4 1 1 2 1 1 2 2
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## 1 1 1 1 2 1 1 1 1 1
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## 1 3 3 2 1 1 1 1 1 1
## 26766 26768 26770 26771 26775 26777 26778 26780 26784 26785
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## 26788 26790 26799 26801 26805 26806 26807 26810 26813 26819
## 1 1 2 1 1 1 1 1 1 2
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## 1 1 1 1 1 1 1 3 1 2
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## 1 1 3 1 1 1 2 2 1 1
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## 1 1 1 1 1 1 1 1 1 1
## 26914 26916 26918 26919 26920 26923 26935 26939 26941 26942
## 2 1 1 2 1 2 1 1 1 3
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## 1 2 2 1 1 1 1 1 1 2
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## 1 1 1 1 1 1 2 2 1 1
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## 1 1 1 1 1 1 1 1 1 1
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## 27163 27164 27166 27169 27172 27177 27178 27179 27181 27186
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## 1 1 1 1 1 2 1 1 1 1
## 27301 27302 27310 27311 27317 27324 27326 27331 27334 27336
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## 27337 27338 27339 27342 27343 27346 27348 27349 27352 27354
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## 27355 27364 27372 27374 27378 27380 27381 27382 27383 27385
## 2 1 2 3 1 1 1 1 1 1
## 27389 27390 27394 27398 27400 27401 27402 27403 27404 27407
## 1 4 2 2 2 1 1 1 1 1
## 27408 27409 27412 27413 27418 27419 27421 27425 27429 27434
## 1 1 2 1 1 1 1 1 2 1
## 27435 27440 27442 27443 27444 27445 27448 27449 27452 27453
## 1 1 1 2 1 1 1 1 1 1
## 27456 27459 27461 27463 27470 27474 27479 27486 27487 27489
## 3 1 1 1 2 1 1 1 1 1
## 27492 27500 27501 27503 27509 27510 27521 27524 27527 27529
## 1 3 1 1 3 1 1 1 3 1
## 27531 27532 27533 27534 27535 27541 27542 27543 27544 27547
## 1 1 1 2 1 1 1 3 1 1
## 27549 27553 27555 27563 27566 27567 27568 27569 27571 27575
## 2 1 1 1 1 2 1 1 1 1
## 27579 27583 27586 27588 27590 27591 27593 27594 27597 27601
## 2 1 1 1 1 2 1 1 1 3
## 27603 27605 27606 27609 27612 27613 27614 27616 27617 27619
## 1 2 1 2 2 1 1 2 1 1
## 27623 27624 27628 27630 27634 27636 27637 27639 27646 27648
## 1 2 1 1 1 1 1 1 1 1
## 27654 27657 27658 27664 27667 27668 27670 27678 27679 27682
## 1 2 1 2 1 1 2 2 2 1
## 27685 27689 27690 27692 27694 27695 27698 27699 27700 27702
## 1 1 1 1 2 1 1 2 2 1
## 27703 27708 27710 27712 27720 27724 27725 27730 27731 27741
## 2 1 1 1 1 2 1 1 1 1
## 27742 27743 27746 27749 27752 27753 27755 27757 27763 27772
## 1 1 1 1 1 1 1 1 1 1
## 27774 27782 27785 27786 27787 27790 27792 27793 27794 27796
## 1 1 1 2 2 1 1 1 1 1
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## 82074 82087 82106 82118 82147 82151 82221 82263 82293 82301
## 1 1 1 1 1 1 1 1 1 1
## 82307 82313 82425 82439 82445 82481 82484 82490 82545 82571
## 1 1 1 1 1 1 1 1 2 1
## 82600 82616 82625 82643 82680 82716 82730 82736 82747 82754
## 1 1 1 1 1 1 1 2 1 1
## 82764 82780 82809 82837 82848 82859 82876 82901 82904 82905
## 1 1 1 1 1 1 1 1 1 2
## 82915 82925 82966 82998 83014 83016 83077 83087 83093 83112
## 1 1 2 1 1 1 2 1 1 1
## 83119 83156 83158 83169 83200 83209 83233 83248 83279 83283
## 1 1 1 1 1 1 1 1 1 1
## 83289 83295 83339 83398 83447 83449 83459 83496 83499 83509
## 1 1 1 1 1 1 1 1 1 1
## 83517 83566 83583 83590 83616 83635 83645 83649 83656 83691
## 1 1 2 1 1 1 1 1 1 1
## 83724 83744 83799 83805 83814 83831 83840 83845 83855 83875
## 1 1 1 1 1 1 1 1 1 1
## 83883 83884 83892 83938 83942 83958 84003 84012 84040 84084
## 1 1 1 1 1 1 1 1 1 1
## 84125 84135 84152 84189 84191 84257 84260 84261 84264 84265
## 1 1 1 1 1 1 1 1 1 1
## 84284 84291 84310 84318 84324 84436 84439 84449 84475 84500
## 1 1 1 1 1 1 1 1 1 1
## 84556 84661 84670 84700 84744 84782 84790 84791 84808 84818
## 2 1 1 1 1 1 1 1 1 1
## 84821 84837 84851 84853 84863 84864 84880 84890 84898 84900
## 1 1 1 1 1 1 1 1 1 1
## 84989 85011 85025 85058 85077 85090 85099 85104 85125 85132
## 1 1 1 1 1 1 1 1 1 1
## 85137 85185 85199 85203 85218 85229 85253 85313 85344 85359
## 1 1 1 1 1 1 2 1 1 1
## 85397 85527 85538 85539 85542 85563 85599 85614 85617 85621
## 1 1 1 1 1 1 1 1 1 1
## 85626 85711 85751 85766 85771 85773 85779 85850 85866 85880
## 1 1 1 1 1 1 1 1 1 1
## 85916 85937 85998 86000 86024 86036 86055 86099 86133 86160
## 1 1 3 1 3 1 1 1 1 1
## 86177 86180 86219 86225 86254 86300 86332 86342 86344 86420
## 1 1 1 1 1 1 1 1 1 1
## 86447 86454 86455 86467 86472 86478 86495 86508 86578 86581
## 1 1 1 2 1 1 1 1 1 2
## 86593 86623 86632 86652 86677 86678 86692 86737 86744 86748
## 1 1 1 1 1 1 1 1 1 1
## 86762 86796 86817 86882 86924 86949 86955 86962 86972 87058
## 1 1 1 1 1 2 1 1 1 1
## 87111 87171 87190 87229 87241 87254 87264 87306 87312 87318
## 1 1 1 1 1 1 1 1 1 1
## 87320 87323 87367 87403 87420 87434 87453 87484 87507 87543
## 1 1 1 1 1 2 1 1 1 1
## 87547 87671 87686 87718 87739 87743 87744 87754 87827 87840
## 1 1 1 1 1 1 1 1 1 1
## 87842 87846 87889 87903 87912 87934 87936 87972 87974 88003
## 1 1 1 1 1 1 1 1 1 1
## 88005 88008 88023 88051 88057 88106 88220 88228 88281 88283
## 1 1 1 1 1 1 1 1 1 1
## 88293 88297 88318 88321 88323 88349 88351 88377 88407 88430
## 1 1 1 1 1 1 1 1 1 1
## 88476 88492 88493 88516 88606 88607 88644 88661 88692 88704
## 1 1 1 1 1 2 1 1 1 1
## 88790 88883 88937 88941 89030 89034 89035 89064 89068 89077
## 1 1 1 1 1 1 1 1 1 1
## 89089 89144 89147 89155 89158 89204 89281 89292 89314 89340
## 1 1 1 1 1 1 1 1 1 1
## 89350 89388 89406 89443 89447 89448 89500 89510 89531 89567
## 1 2 1 1 1 1 1 1 1 1
## 89591 89622 89730 89734 89777 89826 89902 89903 89938 89942
## 1 1 1 1 1 1 1 1 1 1
## 89948 89949 90011 90030 90058 90076 90083 90113 90128 90136
## 1 1 1 1 2 1 2 1 1 1
## 90189 90221 90281 90309 90354 90408 90491 90493 90510 90513
## 1 1 1 1 1 1 2 2 1 1
## 90525 90554 90568 90582 90620 90633 90636 90673 90714 90732
## 1 1 1 1 1 1 1 1 1 2
## 90733 90744 90787 90808 90828 90848 90850 90928 90941 90950
## 1 1 1 1 1 1 1 1 1 1
## 90975 90983 91057 91066 91080 91143 91145 91149 91154 91166
## 1 1 3 1 1 1 1 1 1 1
## 91369 91383 91416 91421 91454 91470 91501 91520 91615 91633
## 1 1 1 1 1 1 1 1 1 1
## 91669 91672 91688 91713 91822 91874 91921 91926 91943 92031
## 1 1 1 1 1 1 1 1 1 1
## 92073 92135 92136 92185 92214 92241 92255 92320 92322 92367
## 1 1 2 1 1 1 2 1 1 1
## 92373 92385 92407 92467 92503 92536 92566 92641 92666 92678
## 2 1 1 1 1 1 2 1 1 1
## 92680 92723 92732 92734 92765 92787 92815 92832 92863 92888
## 1 1 1 1 1 1 1 1 1 1
## 92979 92988 93010 93105 93212 93271 93362 93363 93380 93401
## 1 1 1 1 1 1 1 2 1 1
## 93432 93448 93473 93507 93565 93570 93600 93605 93611 93626
## 1 1 1 1 1 1 1 1 2 1
## 93679 93681 93831 93874 93918 93928 93956 93966 94037 94063
## 1 1 1 1 1 1 1 1 1 1
## 94163 94178 94234 94284 94327 94353 94385 94435 94438 94479
## 1 1 1 1 1 1 1 1 1 1
## 94489 94496 94498 94503 94518 94574 94620 94707 94777 94790
## 1 1 1 1 1 1 1 1 1 1
## 94852 94870 94877 94905 94907 94948 94969 94988 94995 95013
## 1 1 1 1 1 1 1 1 2 1
## 95027 95052 95078 95157 95171 95222 95228 95260 95267 95271
## 1 1 2 1 1 1 1 1 1 1
## 95276 95278 95315 95337 95427 95431 95435 95454 95469 95513
## 1 1 1 1 1 1 1 1 1 1
## 95521 95545 95615 95625 95682 95715 95775 95795 95804 95807
## 1 1 1 1 1 1 1 1 1 1
## 95894 95899 95910 95970 95987 96021 96036 96067 96092 96097
## 1 1 1 1 1 3 1 1 1 1
## 96111 96198 96256 96296 96370 96373 96418 96521 96526 96538
## 1 1 1 1 1 1 1 1 1 1
## 96567 96569 96617 96629 96631 96635 96685 96714 96749 96783
## 1 1 1 1 1 1 1 1 1 1
## 96798 96829 96833 96854 96914 96946 96983 97014 97118 97121
## 1 1 1 1 1 1 1 1 1 1
## 97131 97153 97173 97185 97208 97225 97298 97331 97339 97473
## 2 1 1 1 1 1 1 1 1 1
## 97551 97570 97588 97597 97683 97695 97775 97787 97835 97975
## 1 1 1 1 1 1 1 1 1 1
## 97986 98096 98114 98127 98141 98144 98163 98180 98181 98251
## 1 1 1 1 1 1 1 1 1 1
## 98268 98299 98356 98376 98394 98421 98478 98535 98594 98660
## 1 1 1 1 1 2 1 1 1 1
## 98702 98716 98723 98742 98781 98807 98825 98931 98936 98962
## 2 1 1 1 1 1 1 1 1 1
## 98990 98995 99006 99021 99025 99050 99058 99089 99170 99179
## 1 1 1 1 1 1 1 1 1 1
## 99187 99194 99213 99230 99233 99237 99246 99268 99311 99328
## 1 1 1 1 1 1 1 1 1 1
## 99405 99483 99549 99560 99583 99661 99690 99698 99725 99742
## 1 1 1 1 1 1 1 1 1 1
## 99787 99842 99863 99878 99932 99989 99990 100047 100062 100107
## 1 1 1 1 1 1 1 1 1 1
## 100136 100140 100159 100186 100264 100291 100313 100364 100403 100515
## 1 1 1 1 1 1 1 1 1 1
## 100552 100633 100635 100657 100718 100725 100769 100829 100853 100860
## 1 1 1 1 1 1 1 1 1 1
## 100938 100976 101049 101128 101134 101212 101418 101437 101462 101518
## 1 1 1 1 1 1 1 1 1 1
## 101536 101672 101686 101693 101800 101855 101857 101944 102015 102022
## 1 1 1 1 1 1 1 1 1 2
## 102033 102059 102085 102100 102129 102139 102172 102176 102178 102212
## 1 1 1 1 1 1 1 1 1 1
## 102218 102471 102475 102493 102496 102507 102518 102531 102552 102615
## 1 1 1 1 1 1 1 1 1 1
## 102627 102679 102740 102766 102797 102826 102834 102867 102933 103000
## 1 1 1 1 1 2 1 1 1 1
## 103055 103108 103131 103168 103192 103196 103229 103249 103280 103325
## 1 1 1 1 1 1 1 1 1 1
## 103336 103344 103425 103487 103634 103718 103783 103811 103820 103826
## 1 1 1 1 1 1 1 1 1 1
## 103867 103880 103948 104052 104069 104101 104183 104333 104337 104339
## 1 1 1 1 1 1 1 1 1 1
## 104416 104510 104527 104550 104629 104646 104696 104728 104731 104763
## 1 1 1 1 1 1 1 1 1 1
## 104765 104851 104859 104897 104945 104950 104953 104972 104983 105021
## 1 1 1 1 1 1 1 1 1 1
## 105046 105145 105173 105263 105291 105303 105358 105359 105461 105483
## 1 1 1 1 1 1 1 1 1 1
## 105529 105609 105626 105786 105925 105962 105998 106055 106079 106166
## 1 1 1 1 1 1 1 1 1 1
## 106190 106259 106312 106412 106566 106603 106610 106741 106781 106783
## 1 1 1 1 1 1 1 1 1 1
## 106797 106827 106860 106876 106943 107150 107168 107283 107464 107503
## 1 1 1 1 1 1 1 1 1 1
## 107510 107523 107667 107672 107794 107830 107862 107969 108007 108054
## 1 1 1 1 1 1 1 1 1 1
## 108388 108439 108503 108560 108665 108735 108788 108806 108854 109073
## 1 1 1 1 1 1 1 1 1 1
## 109135 109241 109288 109332 109346 109363 109400 109408 109417 109447
## 1 1 1 1 1 1 2 1 1 1
## 109486 109623 109650 109652 109670 109733 109836 109874 109945 109960
## 1 1 1 1 4 1 1 1 1 1
## 109987 110022 110037 110084 110104 110121 110263 110287 110355 110359
## 1 1 1 1 1 1 1 1 1 1
## 110385 110444 110477 110505 110564 110697 110757 110819 111021 111044
## 1 1 1 1 1 1 1 1 1 1
## 111071 111084 111328 111334 111338 111344 111373 111381 111424 111541
## 2 1 1 1 1 1 1 1 1 2
## 111815 111904 112060 112181 112429 112460 112480 112542 112564 112648
## 1 1 1 1 1 1 1 1 1 1
## 112693 112805 112824 113046 113061 113171 113189 113273 113284 113347
## 1 1 1 1 1 1 1 1 1 1
## 113560 113564 113707 113722 113785 113861 113960 114083 114087 114244
## 1 1 1 1 1 1 1 1 1 1
## 114335 114350 114393 114540 114592 114594 114642 114731 114791 115134
## 1 1 1 1 1 1 1 1 1 1
## 115261 115281 115507 115605 115787 115795 115866 115909 116098 116210
## 1 1 1 1 2 1 1 1 1 1
## 116256 116343 116404 116582 116726 116789 116824 116933 116952 116953
## 1 1 1 1 1 1 2 1 1 1
## 117135 117260 117323 117325 117371 117569 117613 117707 117754 117757
## 1 1 1 1 1 1 1 1 1 1
## 117783 117812 117825 118084 118188 118204 118220 118239 118491 118609
## 1 1 1 1 1 1 1 1 1 1
## 118656 118750 118891 119010 119229 119278 119310 119395 119570 119875
## 1 1 1 2 1 1 1 1 1 1
## 120116 120262 120317 120503 120560 120583 120587 120732 120828 120930
## 1 1 1 1 1 1 1 1 1 1
## 121021 121031 121171 121230 121312 121395 121441 121651 121786 121818
## 1 1 1 1 3 1 1 1 1 1
## 122042 122188 122394 122476 122487 122546 122552 122680 122734 122776
## 1 1 1 1 1 1 1 1 1 1
## 122810 122823 122831 122874 122895 122913 122976 123319 123328 123521
## 1 1 1 1 1 2 2 1 1 1
## 123551 123658 123726 123752 123885 123888 124145 124170 124175 124244
## 1 1 1 1 1 1 1 1 1 1
## 124435 124449 124620 124775 124804 125087 125279 125365 125439 125508
## 1 1 1 1 1 1 1 1 1 1
## 125768 125919 126030 126063 126218 126275 126482 126554 126744 126810
## 2 1 1 1 1 1 1 1 1 1
## 126933 126989 127056 127334 127451 127453 127464 127481 127667 127687
## 1 1 1 1 1 1 1 1 1 1
## 127901 128230 128566 128577 128726 129074 129242 129291 129331 129379
## 1 1 1 1 1 1 2 1 1 1
## 129383 129814 129958 129972 130333 130619 130830 130869 131208 131305
## 1 1 1 1 1 1 1 1 1 1
## 131361 131435 131436 131458 131535 131895 131998 132255 132418 132544
## 1 1 1 1 1 1 1 1 1 1
## 132628 132652 132771 132810 133001 133132 133147 133533 134212 134346
## 1 1 1 1 1 1 1 1 1 1
## 134732 135529 135739 135868 135869 136155 136328 136364 137040 137042
## 1 1 1 1 1 1 1 1 1 1
## 137346 137427 137706 137868 137923 138529 138669 138742 139046 139072
## 1 1 1 1 1 1 1 1 1 1
## 139367 139730 139754 139758 139860 139898 139923 139964 140002 140056
## 1 1 1 1 1 1 1 1 1 1
## 140086 140337 140641 140889 140994 141088 141117 141288 141458 142167
## 1 1 1 1 1 1 1 2 1 1
## 142178 142216 142751 142831 143192 143743 144067 144470 144512 144593
## 1 1 1 1 1 1 1 1 1 1
## 144726 145095 145162 145220 145435 145533 145692 145711 146066 146391
## 1 1 1 1 1 1 1 1 1 1
## 146826 146847 147904 147964 148029 148445 148724 148835 149145 149164
## 1 1 1 1 1 1 1 1 1 1
## 149357 149568 149638 149957 150492 150711 150926 151139 151208 151255
## 1 1 1 1 1 2 1 1 1 1
## 152351 152517 152572 152818 153477 153721 154120 154255 156049 156101
## 1 1 1 1 1 1 1 1 1 1
## 156301 156344 156520 157366 157513 157566 157969 158245 158424 159488
## 1 1 2 1 1 1 1 1 1 1
## 159828 160424 160701 160890 161198 161886 161990 162635 162754 162759
## 1 1 1 1 1 1 1 1 1 1
## 163182 163269 163506 164897 165241 165749 166288 167042 167139 167316
## 1 1 1 1 1 2 1 1 1 1
## 168762 169035 170599 171691 172191 172280 173471 173493 174397 174817
## 1 1 1 1 1 1 1 1 1 1
## 175020 175853 179395 179491 180509 180835 181698 182086 182971 184389
## 1 1 1 1 1 1 1 1 1 1
## 185390 186394 188732 189955 190060 190721 191272 191828 192082 194165
## 1 1 1 1 1 1 1 2 1 1
## 194522 195951 196003 196046 197012 197160 197817 200476 202237 202784
## 1 2 1 1 1 1 1 1 1 1
## 204612 206935 208223 210787 212450 213166 217122 217386 220073 225915
## 1 1 1 1 1 1 1 1 1 1
## 229316 229347 233738 233881 239581 243922 250148 251444 257959 265129
## 1 1 1 1 1 1 1 1 1 1
## 288049 292351 312144 320085
## 1 1 1 1here we observe that the values are from negative to positive.
finding the less than value 6000 & 30000
NROW(data[which(data$x236<=6000),])## [1] 70127here we observed 70% are less than 6000
NROW(data[which(data$x236<=30000),])## [1] 89424here we observed 89% are less than 30000
Now visualize the statistics
Histogram
hist(data$x236,
main = "Histogram of x236",
xlab = "x236")
abline(v =mean(data$x236, na.rm = TRUE),col = "red",
lwd = 2)
Boxplot
boxplot(data$x236,
main = toupper("Boxplot of x236"),
ylab = "x236",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x236, na.rm = TRUE)
plot(d, main = "Kernel density of x236")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x236, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has some density there is no missing values most f the values lies in b0 to 4 and the bandwiidth is 1.5,the skewness is a postive because the tail will have the large values
summary of the variable x237
summary(data$x237)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 21.0 83.0 123.8 192.0 718.0 36744observe that values from 0 to 718, median is 83 & mean is 123.8 ,there is 36 % of missing values.
Description of the variable x237
describe(data$x237)## data$x237
## n missing distinct Info Mean Gmd .05 .10
## 63256 36744 665 1 123.8 132.5 4 7
## .25 .50 .75 .90 .95
## 21 83 192 312 374
##
## lowest : 0 1 2 3 4, highest: 700 704 710 713 718Here the kurtosis is positive and its has large data in tails.
Total number of values excluding NA
NROW(data$x237) - NROW(data[which(is.na(data$x237)),])## [1] 63256Here the value is 63256 because there is 36% of missing values are exists.
table(data$x237)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 269 698 892 1146 1090 1030 1102 1045 849 874 634 665 672 625 598
## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## 600 563 608 524 539 492 552 414 503 488 464 426 439 398 391
## 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## 345 386 371 319 324 388 352 313 307 296 294 231 238 228 213
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 219 247 251 235 225 215 228 236 225 204 212 211 198 218 249
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## 263 247 227 227 212 208 185 204 183 173 185 177 198 153 168
## 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## 153 150 131 115 121 116 92 106 130 106 115 109 111 103 125
## 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104
## 109 107 111 103 116 148 122 147 150 185 198 164 170 188 166
## 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
## 176 157 166 177 204 180 213 207 211 188 196 180 160 180 211
## 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
## 200 197 176 195 200 178 163 185 179 153 137 164 130 190 184
## 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
## 185 152 167 170 138 179 115 129 170 148 162 126 141 142 148
## 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164
## 149 128 159 116 140 105 140 121 140 159 122 116 122 117 140
## 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
## 103 109 112 129 120 119 125 126 126 121 141 119 123 104 115
## 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
## 116 116 71 126 96 130 154 124 121 105 112 129 140 124 110
## 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
## 135 116 117 118 130 136 130 97 124 125 129 102 97 110 102
## 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 105 118 124 118 81 101 99 86 67 79 89 72 86 98 74
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
## 73 64 87 86 82 77 79 62 53 85 86 72 77 57 51
## 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254
## 81 82 65 87 69 88 76 67 85 82 62 58 106 105 78
## 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
## 93 72 80 65 68 67 58 53 82 95 90 71 71 80 78
## 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284
## 71 63 105 56 55 95 109 59 62 55 71 58 55 70 70
## 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
## 49 56 83 67 79 64 58 50 43 50 48 59 68 38 42
## 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
## 64 66 41 55 32 46 51 49 56 68 32 44 55 66 63
## 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329
## 41 72 66 54 61 67 55 44 35 53 93 52 44 48 50
## 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344
## 80 69 35 37 38 36 59 37 45 45 42 35 57 76 59
## 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
## 39 36 56 56 71 52 41 66 49 41 41 66 70 38 59
## 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374
## 69 59 66 49 45 58 63 60 49 37 31 17 28 30 26
## 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
## 28 36 30 19 35 40 30 28 45 43 30 31 29 28 22
## 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404
## 26 28 41 49 21 24 25 20 32 17 26 23 25 27 21
## 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419
## 19 20 21 28 19 14 12 23 20 27 18 16 13 17 15
## 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
## 15 30 19 13 16 15 25 15 24 14 32 15 24 15 13
## 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449
## 15 13 14 13 11 16 19 18 34 18 13 17 19 15 18
## 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 13 21 25 17 19 11 16 13 17 16 18 20 11 16 25
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479
## 17 19 10 14 14 15 19 16 13 17 17 6 17 16 8
## 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494
## 16 5 12 8 14 13 12 12 9 11 10 11 9 6 8
## 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509
## 13 12 11 8 5 9 8 9 9 11 7 4 7 5 9
## 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524
## 5 15 12 12 7 6 4 14 10 4 6 10 7 11 14
## 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539
## 4 9 8 6 11 2 15 9 4 8 7 12 9 7 6
## 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554
## 4 7 4 4 6 5 8 5 6 5 10 3 2 7 5
## 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569
## 2 3 10 4 7 4 3 7 7 6 5 5 6 7 7
## 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
## 7 7 6 7 6 3 3 5 5 2 5 3 4 6 2
## 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599
## 4 6 4 2 4 6 4 3 9 1 2 2 7 10 2
## 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614
## 2 7 1 6 5 7 2 1 5 2 1 2 2 1 2
## 615 616 617 619 620 621 622 623 624 625 626 627 628 629 630
## 4 4 2 1 2 2 1 1 2 1 2 2 1 1 4
## 632 633 634 635 636 637 641 643 644 646 647 648 649 650 653
## 2 1 8 1 2 1 1 2 1 5 2 3 1 1 1
## 657 658 665 666 670 671 679 680 682 691 692 693 694 695 696
## 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 700 704 710 713 718
## 2 1 1 1 1finding the less than value 300 & 600
NROW(data[which(data$x237<=300),])## [1] 56336here we observed 56% less than 300
NROW(data[which(data$x237<=600),])## [1] 63128here we observed 63% are less than 600
Now visualize the statistics
##Histogram
hist(data$x237,
main = "Histogram of x237",
xlab = "x237")
abline(v =mean(data$x237, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 600 has max values.
Boxplot
boxplot(data$x237,
main = toupper("Boxplot of x237"),
ylab = "x237",
col = "red")
## we found the outliers.
Density Plot
d <- density(data$x237, na.rm = TRUE)
plot(d, main = "Kernel density of x237")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x237, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is 36% of missing values most of the values lies in b0 to 600 and the skewness is a postive because the tail will have the large values. so th vaiable has descrete values.
summary of the variable x238
summary(data$x238)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 3.00 10.00 38.13 38.00 704.00 36744that values from 0 to 704,median is 10 & mean is 38.13 , there is 36% of missing values.
Description of the variable x238
describe(data$x238)## data$x238
## n missing distinct Info Mean Gmd .05 .10
## 63256 36744 517 0.998 38.13 54.15 1 1
## .25 .50 .75 .90 .95
## 3 10 38 118 178
##
## lowest : 0 1 2 3 4, highest: 646 653 665 700 704Here the kurtosis is positive and its hass large dat in tail.
Total number of values excluding NA
NROW(data$x238) - NROW(data[which(is.na(data$x238)),])## [1] 63256Here the value is 63256 because there is 37% of missing values
table(data$x238)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 2936 4786 4425 4195 3509 3051 2573 2065 1667 1443 1228 1295 1139 1000 919
## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## 828 816 772 683 658 516 517 511 563 502 484 441 446 455 375
## 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## 373 363 298 250 321 359 311 261 246 257 237 213 200 187 192
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
## 171 215 189 187 175 149 179 180 179 161 175 156 150 139 178
## 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## 198 164 153 164 161 141 105 126 80 109 105 104 97 92 105
## 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## 107 73 70 59 84 78 73 69 85 74 65 58 71 71 90
## 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104
## 70 73 83 63 69 87 77 91 73 90 111 81 85 89 91
## 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
## 97 93 88 81 110 86 104 92 86 89 98 97 76 83 92
## 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
## 84 86 79 99 82 69 70 72 66 49 55 58 56 76 85
## 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
## 92 63 66 51 55 72 37 60 49 47 61 40 34 55 47
## 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164
## 50 35 56 35 28 36 44 36 42 51 40 48 35 36 43
## 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
## 30 37 38 38 48 43 32 33 34 30 33 34 32 31 28
## 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
## 45 39 24 35 35 29 32 38 40 19 30 24 35 30 30
## 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
## 23 25 19 25 32 30 30 21 25 31 27 22 29 18 21
## 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 22 28 25 23 13 18 29 23 10 15 21 17 17 20 21
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
## 13 16 13 20 14 19 13 22 16 15 12 11 18 11 9
## 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254
## 26 20 22 23 23 16 19 10 12 16 13 12 16 26 12
## 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
## 14 10 17 10 10 7 16 18 17 20 19 18 14 18 16
## 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284
## 14 9 19 18 10 12 25 13 12 11 12 13 10 14 15
## 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
## 6 10 13 15 19 14 7 13 3 4 8 7 10 6 9
## 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
## 9 6 6 11 8 8 8 11 9 10 4 6 9 14 10
## 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329
## 7 11 15 5 15 12 10 6 7 5 18 9 13 3 4
## 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344
## 7 11 7 1 6 3 3 7 9 10 6 4 9 9 6
## 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
## 5 2 13 11 10 11 6 8 8 7 10 4 13 9 5
## 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374
## 11 7 13 5 4 6 8 8 4 6 4 4 6 2 4
## 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
## 2 3 6 2 3 6 4 4 7 3 3 5 2 3 5
## 390 391 392 393 394 396 397 398 399 400 401 402 403 404 405
## 1 3 3 6 3 5 3 6 2 2 1 2 3 2 2
## 406 407 408 409 410 412 413 414 415 417 420 421 425 426 427
## 1 3 5 3 6 2 1 2 4 2 1 5 4 2 2
## 428 430 431 433 434 435 436 437 442 443 444 447 448 449 450
## 5 2 1 2 3 1 3 3 1 5 2 3 1 2 1
## 451 452 455 456 457 458 459 460 461 464 465 466 467 468 469
## 3 3 1 1 1 3 3 1 1 3 1 2 2 2 2
## 470 471 472 473 474 475 476 477 480 482 484 485 486 487 488
## 5 1 2 2 2 2 1 2 1 2 1 1 1 3 1
## 489 490 492 495 496 497 498 499 502 504 505 508 509 512 513
## 2 5 1 1 3 1 2 1 1 2 1 1 1 2 2
## 516 519 520 523 527 528 532 534 536 538 539 541 546 547 552
## 1 1 1 2 1 1 2 1 2 1 1 3 1 1 1
## 553 557 558 559 563 565 568 572 573 577 580 581 583 601 603
## 1 3 1 1 2 1 1 1 2 1 2 2 1 1 2
## 608 626 646 653 665 700 704
## 1 1 1 1 1 1 1finding the less than value 300 & 700
NROW(data[which(data$x238<=300),])## [1] 62373here we observed 62% less than 300
NROW(data[which(data$x238<=700),])## [1] 63255here we observed 63% are less than 700
Now visualize the statistics
Histogram
hist(data$x238,
main = "Histogram of x238",
xlab = "x238")
abline(v =mean(data$x238, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 400 has max values.
Boxplot
boxplot(data$x238,
main = toupper("Boxplot of x238"),
ylab = "x238",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x238, na.rm = TRUE)
plot(d, main = "Kernel density of x238")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x238, na.rm = TRUE),col = "royalblue",
lwd = 2)
## observation:- ## Here 0 has more density there is 36% missing values most of the values are lies in b0 to 400 and the skewness is a postive because the tail will have the large values. so this variable has a descrete values.
summary of the variable x239
summary(data$x239)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 14.00 46.50 75.33 112.33 704.00 36744observe that values from 0 to 704, median is 46 & mean is 75.33
Description of the variable x239
describe(data$x239)## data$x239
## n missing distinct Info Mean Gmd .05 .10
## 63256 36744 7179 1 75.33 82.06 3.000 5.118
## .25 .50 .75 .90 .95
## 14.000 46.500 112.333 185.500 240.682
##
## lowest : 0.0000 0.2000 0.2500 0.2857 0.3333
## highest: 646.0000 653.0000 665.0000 700.0000 704.0000Here the kurtosis is positive and its has large data in tails.
Total number of values excluding NA
NROW(data$x239) - NROW(data[which(is.na(data$x239)),])## [1] 63256Here the value is 63256 because there is 36% of missing values.
table(data$x239)##
## 0 0.2 0.25 0.2857 0.3333 0.4 0.4285 0.5
## 269 1 1 1 6 1 1 31
## 0.5999 0.6666 0.75 0.8 0.8571 0.9333 1 1.1428
## 2 10 5 3 2 1 694 1
## 1.2 1.2222 1.2307 1.25 1.3333 1.3999 1.5 1.5555
## 2 1 1 5 22 3 84 1
## 1.5714 1.6 1.6666 1.75 1.8 1.8333 1.8571 1.8888
## 1 4 28 9 5 3 2 1
## 1.909 2 2.1 2.1111 2.125 2.1428 2.1666 2.1818
## 1 866 1 1 1 1 4 1
## 2.2 2.2173 2.25 2.2727 2.2857 2.3333 2.3999 2.4285
## 3 1 12 1 2 39 8 3
## 2.4375 2.4545 2.5 2.5454 2.5555 2.5714 2.5833 2.6
## 1 2 137 1 2 1 1 14
## 2.6363 2.6666 2.7142 2.7222 2.7272 2.75 2.7647 2.7777
## 1 40 2 1 1 22 1 1
## 2.7999 2.8076 2.8333 2.8571 2.875 2.8823 2.8999 2.9166
## 14 1 4 5 2 1 1 1
## 2.923 3 3.0344 3.1 3.1111 3.1428 3.1666 3.2
## 1 1065 1 1 1 1 5 19
## 3.2142 3.2222 3.25 3.2857 3.2999 3.3333 3.3571 3.375
## 1 2 29 6 1 61 1 1
## 3.3846 3.3999 3.4285 3.4444 3.4545 3.5 3.5555 3.5714
## 1 7 4 2 2 180 2 3
## 3.5789 3.6 3.625 3.6363 3.6666 3.6764 3.6923 3.7142
## 1 7 6 2 61 1 1 3
## 3.75 3.7619 3.7777 3.7999 3.8181 3.8333 3.85 3.8571
## 22 1 2 10 1 5 1 3
## 3.875 3.8888 3.8999 4 4.0476 4.0909 4.0999 4.1111
## 1 1 1 971 1 1 1 2
## 4.125 4.1428 4.1666 4.1818 4.2 4.2222 4.25 4.2727
## 3 2 11 1 9 4 30 1
## 4.2857 4.2999 4.3333 4.375 4.4 4.4166 4.4285 4.4444
## 7 1 71 2 10 1 2 2
## 4.5 4.5238 4.5454 4.5555 4.5714 4.5833 4.5999 4.6153
## 199 1 2 1 3 1 12 1
## 4.6363 4.6666 4.6875 4.7142 4.7272 4.738 4.75 4.7777
## 1 63 1 3 3 1 26 2
## 4.7999 4.8333 4.8571 4.8888 4.9 4.909 4.9677 5
## 11 3 5 1 1 1 1 914
## 5.0714 5.0909 5.0999 5.1111 5.125 5.1428 5.1666 5.2
## 1 2 1 2 1 3 9 22
## 5.2142 5.2222 5.24 5.25 5.2727 5.2857 5.2999 5.3333
## 1 2 1 50 2 4 1 54
## 5.3636 5.375 5.3846 5.4 5.4166 5.4285 5.4736 5.5
## 1 1 2 15 1 5 1 192
## 5.5294 5.5384 5.5499 5.5555 5.5714 5.5999 5.625 5.6363
## 1 1 1 2 3 20 1 1
## 5.6666 5.7 5.7142 5.75 5.7692 5.7777 5.7857 5.7999
## 80 2 4 25 1 1 1 11
## 5.8181 5.8333 5.8421 5.8571 5.875 5.8888 5.9 5.909
## 1 8 1 6 2 2 2 2
## 6 6.0526 6.0625 6.0714 6.0769 6.0833 6.0909 6.0999
## 848 1 1 1 3 1 1 1
## 6.1111 6.125 6.1333 6.1428 6.1666 6.2 6.2222 6.25
## 6 4 2 8 3 17 1 23
## 6.2631 6.2777 6.2857 6.2999 6.3076 6.3333 6.3478 6.3571
## 1 1 4 5 1 72 1 2
## 6.375 6.3846 6.4 6.4285 6.4347 6.4444 6.4545 6.4583
## 2 1 13 4 1 2 1 1
## 6.5 6.5454 6.5555 6.5714 6.5999 6.625 6.6363 6.6666
## 192 1 3 5 19 2 1 68
## 6.6923 6.7 6.7142 6.75 6.7777 6.7857 6.7999 6.8333
## 2 2 6 29 2 1 14 8
## 6.8571 6.875 6.8888 6.923 6.9285 6.9333 7 7.0625
## 5 1 3 2 1 1 772 1
## 7.0714 7.1052 7.125 7.1333 7.1428 7.1538 7.1578 7.1666
## 1 1 1 2 5 1 1 8
## 7.1818 7.2 7.2222 7.25 7.2758 7.2857 7.3181 7.3333
## 1 13 6 26 1 3 1 64
## 7.375 7.4 7.4166 7.4285 7.4444 7.4615 7.4666 7.4736
## 5 16 1 5 1 1 1 1
## 7.5 7.5333 7.5384 7.5555 7.5714 7.5999 7.625 7.6363
## 167 1 1 4 7 12 7 1
## 7.6666 7.6842 7.6923 7.7 7.7142 7.75 7.7777 7.7857
## 68 1 2 6 7 29 2 1
## 7.7894 7.7999 7.8125 7.8181 7.8333 7.8461 7.8571 7.8888
## 1 15 1 1 11 2 2 4
## 7.9 7.9166 7.9285 8 8.0555 8.0769 8.0833 8.0909
## 2 2 1 645 2 1 1 2
## 8.0999 8.1176 8.125 8.1538 8.1666 8.1818 8.1999 8.2142
## 2 1 1 2 9 2 14 1
## 8.2272 8.2307 8.25 8.2666 8.2857 8.3 8.3333 8.3571
## 1 1 26 1 8 2 67 1
## 8.375 8.3846 8.4 8.421 8.4285 8.4444 8.4545 8.5
## 2 1 13 1 4 7 2 188
## 8.5217 8.5263 8.5333 8.5454 8.5555 8.5714 8.5789 8.5833
## 1 1 1 1 4 2 1 2
## 8.5999 8.6111 8.619 8.625 8.6363 8.6666 8.6799 8.6923
## 16 1 1 7 2 52 1 1
## 8.6999 8.7142 8.7272 8.7333 8.75 8.7619 8.7647 8.7777
## 1 4 1 1 32 1 1 4
## 8.8 8.8333 8.8571 8.875 8.8888 8.9 8.923 8.9333
## 16 8 1 3 2 3 1 2
## 8.9375 9 9.05 9.0526 9.0833 9.1052 9.1111 9.125
## 2 593 1 1 2 1 3 5
## 9.1333 9.1428 9.15 9.1666 9.1818 9.1875 9.1999 9.2222
## 1 8 1 10 2 1 17 5
## 9.238 9.25 9.2727 9.2857 9.3 9.3333 9.3636 9.375
## 1 31 1 6 2 68 1 4
## 9.3846 9.4 9.4166 9.4285 9.4444 9.4545 9.4736 9.5
## 2 11 1 3 1 2 1 144
## 9.5294 9.55 9.5714 9.5789 9.5833 9.5999 9.625 9.6363
## 1 1 6 1 1 11 7 3
## 9.6666 9.6999 9.7142 9.7272 9.7333 9.75 9.7647 9.7777
## 62 3 8 2 1 37 1 5
## 9.8 9.8181 9.8235 9.8333 9.8461 9.8571 9.875 9.8888
## 11 2 1 12 1 4 6 1
## 9.9285 10 10.0833 10.0909 10.1 10.125 10.1428 10.1666
## 1 469 1 1 3 5 5 12
## 10.1818 10.1999 10.2142 10.2222 10.25 10.2666 10.2857 10.3333
## 1 20 2 1 24 3 5 53
## 10.3571 10.375 10.4 10.409 10.4285 10.4375 10.4444 10.4666
## 1 2 17 1 7 1 3 1
## 10.5 10.5238 10.5384 10.5454 10.5555 10.5714 10.5882 10.6
## 153 1 1 1 2 5 1 13
## 10.6153 10.625 10.6363 10.6666 10.6842 10.6875 10.7142 10.7272
## 1 5 1 66 1 2 7 2
## 10.75 10.7647 10.7692 10.7741 10.7777 10.7857 10.8 10.8181
## 25 1 1 1 4 1 10 1
## 10.8333 10.85 10.8571 10.8666 10.875 10.8888 10.909 10.9166
## 12 1 3 1 2 3 1 2
## 11 11.0666 11.0689 11.0833 11.0909 11.1 11.1111 11.125
## 463 2 1 1 3 1 3 2
## 11.1333 11.1428 11.1666 11.1818 11.1904 11.1999 11.2222 11.25
## 1 3 9 2 1 16 3 25
## 11.2727 11.2857 11.3 11.3043 11.3076 11.3125 11.3333 11.35
## 3 8 3 1 1 1 55 1
## 11.375 11.4 11.4117 11.421 11.4285 11.4545 11.5 11.5555
## 6 15 1 1 7 1 136 5
## 11.5714 11.5882 11.6 11.625 11.6363 11.647 11.6666 11.6999
## 4 1 24 5 3 1 68 1
## 11.7142 11.7272 11.7333 11.7428 11.75 11.7777 11.8 11.8333
## 5 1 1 1 30 5 18 8
## 11.8461 11.8571 11.875 11.8888 11.9 11.909 11.9714 12
## 1 6 5 3 1 2 1 447
## 12.0384 12.0666 12.0714 12.125 12.1363 12.1428 12.1538 12.1578
## 1 1 2 2 1 4 1 1
## 12.1666 12.1764 12.1904 12.1999 12.2142 12.2222 12.2307 12.25
## 11 1 1 17 2 2 1 22
## 12.2857 12.3076 12.3333 12.3529 12.3636 12.375 12.3809 12.4
## 5 1 68 1 1 4 1 8
## 12.4166 12.4285 12.4545 12.5 12.5294 12.5333 12.5384 12.5416
## 2 6 1 129 1 2 1 1
## 12.5555 12.5714 12.6 12.6153 12.6166 12.625 12.6428 12.6666
## 3 4 12 2 1 4 1 56
## 12.6923 12.6999 12.7142 12.75 12.7692 12.7777 12.7857 12.8
## 1 1 3 19 2 2 1 6
## 12.8095 12.8125 12.8181 12.8333 12.8461 12.8571 12.875 12.8888
## 1 1 2 13 2 4 2 1
## 12.909 12.913 12.9333 12.9375 12.9444 13 13.1 13.1111
## 3 1 1 1 1 405 1 2
## 13.125 13.1333 13.1428 13.1666 13.1764 13.1818 13.1999 13.2222
## 3 1 5 8 1 1 18 1
## 13.2307 13.25 13.2666 13.2727 13.2857 13.3 13.3076 13.3333
## 1 20 1 1 7 3 2 55
## 13.3636 13.375 13.4 13.4285 13.4444 13.4545 13.5 13.5238
## 2 5 16 3 2 1 126 1
## 13.5333 13.5384 13.5454 13.5555 13.5714 13.5833 13.6 13.6086
## 1 1 1 3 4 2 13 2
## 13.6111 13.625 13.6363 13.647 13.6666 13.6923 13.7142 13.75
## 1 5 2 1 49 1 2 29
## 13.7777 13.7857 13.8 13.8333 13.8571 13.875 13.8888 13.9
## 2 1 15 8 8 4 2 3
## 13.9166 13.923 13.9285 14 14.0476 14.0606 14.0909 14.1
## 2 1 1 365 1 1 1 4
## 14.1111 14.125 14.1333 14.1428 14.1666 14.1999 14.2222 14.2352
## 4 2 1 1 9 8 1 1
## 14.25 14.2727 14.2857 14.3 14.3333 14.3636 14.3703 14.375
## 22 4 6 1 46 1 1 3
## 14.4 14.4285 14.4444 14.4523 14.4545 14.4615 14.5 14.5555
## 23 1 2 1 2 2 122 3
## 14.5714 14.5882 14.6 14.625 14.6363 14.65 14.6666 14.6999
## 7 1 14 2 1 1 47 3
## 14.7142 14.7272 14.75 14.7692 14.7777 14.8 14.8181 14.8333
## 5 2 22 1 1 9 1 10
## 14.8571 14.8666 14.875 14.8888 14.9 14.923 14.9285 14.931
## 5 1 1 1 1 1 2 1
## 15 15.0714 15.1 15.1111 15.125 15.1428 15.1538 15.1666
## 351 3 4 1 1 3 2 22
## 15.1818 15.1999 15.2121 15.2222 15.2352 15.25 15.2608 15.2857
## 1 11 1 4 1 23 1 3
## 15.3 15.3076 15.3333 15.3684 15.375 15.4 15.4166 15.4285
## 2 1 47 1 3 10 2 5
## 15.4324 15.4375 15.4545 15.5 15.5555 15.5714 15.6 15.625
## 1 1 1 129 2 3 9 2
## 15.6363 15.6666 15.6999 15.7142 15.75 15.7692 15.7777 15.7894
## 1 47 1 4 20 1 1 1
## 15.8 15.8181 15.8333 15.8571 15.8695 15.875 15.8888 15.9
## 13 1 12 6 1 3 1 1
## 15.9166 15.923 16 16.0357 16.0588 16.0625 16.0666 16.0833
## 2 1 353 1 1 1 1 1
## 16.0909 16.1 16.1111 16.125 16.1428 16.1666 16.1764 16.1818
## 1 3 2 3 2 5 1 2
## 16.1999 16.2222 16.25 16.2666 16.2727 16.2857 16.2941 16.3
## 12 4 19 1 3 6 1 4
## 16.3333 16.375 16.3999 16.4285 16.4444 16.4545 16.5 16.5333
## 49 5 7 4 1 2 107 1
## 16.5384 16.5454 16.5625 16.5714 16.5833 16.6 16.6153 16.625
## 2 3 1 3 1 9 1 1
## 16.6666 16.6999 16.7058 16.7142 16.7169 16.7222 16.7333 16.75
## 43 2 1 9 1 1 1 23
## 16.7692 16.8 16.8181 16.8333 16.8571 16.875 16.8888 16.909
## 1 18 1 7 5 6 3 2
## 16.9166 17 17.0555 17.0588 17.0666 17.0714 17.0909 17.1111
## 1 355 1 1 1 1 2 3
## 17.125 17.1428 17.1499 17.1666 17.1818 17.1999 17.2142 17.2222
## 3 5 2 6 1 13 1 4
## 17.25 17.2857 17.3 17.3076 17.3333 17.375 17.3999 17.4166
## 27 7 1 2 51 5 13 1
## 17.4285 17.4375 17.4444 17.4545 17.4615 17.4666 17.4705 17.5
## 1 1 5 1 1 1 1 79
## 17.5454 17.5714 17.5833 17.5937 17.6 17.6111 17.6153 17.625
## 1 6 1 1 6 1 2 3
## 17.6363 17.647 17.6666 17.6999 17.7142 17.7272 17.75 17.7777
## 1 1 48 2 4 2 20 1
## 17.7857 17.8 17.8125 17.8181 17.8333 17.8571 17.8666 17.875
## 2 8 1 2 12 6 1 3
## 17.8888 17.8999 17.9166 17.923 17.9375 18 18.0416 18.0434
## 4 1 1 1 1 338 1 1
## 18.0588 18.0666 18.0833 18.1111 18.125 18.1428 18.1666 18.1999
## 1 1 1 2 1 3 6 13
## 18.25 18.2631 18.2666 18.2727 18.28 18.2857 18.3 18.3076
## 24 2 1 3 1 6 1 1
## 18.3125 18.3333 18.3529 18.3571 18.375 18.3928 18.3999 18.4166
## 1 35 1 1 1 1 16 1
## 18.4285 18.4444 18.5 18.5384 18.5454 18.5555 18.5714 18.5833
## 1 3 97 1 2 4 3 2
## 18.6 18.625 18.6428 18.6666 18.6875 18.6999 18.7058 18.7142
## 11 5 2 43 1 2 1 4
## 18.7272 18.7333 18.75 18.7857 18.8 18.8181 18.8333 18.8461
## 1 1 14 1 7 2 4 1
## 18.8571 18.875 18.8823 18.8888 18.8999 18.9166 18.9285 19
## 5 1 1 1 2 2 1 276
## 19.05 19.0526 19.0714 19.0769 19.0833 19.1 19.1111 19.1428
## 1 1 1 1 1 2 1 4
## 19.1666 19.1999 19.2105 19.2222 19.2352 19.25 19.2857 19.3
## 5 11 1 2 1 15 4 1
## 19.3157 19.3333 19.3636 19.375 19.3999 19.4166 19.4285 19.4347
## 1 47 2 2 9 2 4 1
## 19.4444 19.4545 19.4705 19.5 19.5454 19.5555 19.5714 19.5833
## 3 1 1 96 1 1 2 2
## 19.6 19.625 19.6363 19.647 19.6666 19.6818 19.7142 19.75
## 6 3 1 1 47 1 2 20
## 19.7777 19.8 19.8181 19.8333 19.8461 19.8571 19.8666 19.8823
## 3 4 1 5 1 5 1 2
## 19.8888 19.8999 19.909 19.9285 20 20.0666 20.0909 20.1
## 1 1 1 1 295 1 1 1
## 20.125 20.1428 20.1578 20.1666 20.1818 20.1875 20.1999 20.2142
## 2 2 1 5 1 1 11 1
## 20.2222 20.25 20.2857 20.3 20.3125 20.3333 20.3488 20.3571
## 1 23 1 3 1 33 1 1
## 20.3636 20.3999 20.4285 20.4545 20.4615 20.5 20.5294 20.5555
## 1 10 2 1 1 94 1 3
## 20.5714 20.5833 20.6 20.625 20.6363 20.6666 20.6842 20.6969
## 2 3 11 1 1 51 1 1
## 20.6999 20.7142 20.75 20.7777 20.8 20.8181 20.8333 20.8571
## 1 5 22 2 13 1 8 5
## 20.875 20.8888 20.8999 20.909 20.923 21 21.0833 21.1
## 1 4 2 1 1 285 1 1
## 21.1111 21.125 21.1428 21.1666 21.1818 21.1999 21.2222 21.25
## 5 4 3 4 1 8 2 15
## 21.2666 21.2727 21.2857 21.3 21.3333 21.375 21.3999 21.4285
## 1 1 2 2 33 2 14 4
## 21.4375 21.4444 21.4545 21.4666 21.5 21.5555 21.5666 21.5714
## 1 2 1 1 102 3 1 2
## 21.6 21.625 21.6363 21.6666 21.6875 21.6999 21.7142 21.7222
## 8 4 1 35 1 2 10 1
## 21.7272 21.75 21.7692 21.7777 21.8 21.8181 21.8333 21.8461
## 1 13 1 2 8 1 6 1
## 21.8571 21.875 21.8888 21.8999 21.909 21.9444 22 22.0666
## 3 2 2 1 3 1 247 1
## 22.0833 22.0869 22.0909 22.1 22.1052 22.1111 22.1176 22.125
## 1 1 1 2 1 1 1 5
## 22.1428 22.1666 22.1764 22.1818 22.1999 22.2307 22.25 22.2727
## 6 3 1 1 15 1 23 2
## 22.2857 22.3 22.3076 22.3333 22.35 22.375 22.3999 22.4285
## 4 2 2 29 1 3 11 7
## 22.4545 22.4814 22.5 22.5357 22.5454 22.5714 22.6 22.625
## 3 1 80 1 1 3 9 3
## 22.6666 22.6999 22.7142 22.75 22.7777 22.8 22.8333 22.8571
## 41 2 4 21 3 11 5 11
## 22.875 22.8888 22.923 22.9285 23 23.0666 23.0909 23.1
## 2 2 1 1 273 1 2 1
## 23.1052 23.1111 23.125 23.1428 23.1666 23.1818 23.1875 23.1999
## 1 2 2 3 9 2 1 11
## 23.2222 23.25 23.2592 23.2727 23.2857 23.3 23.3125 23.3333
## 3 18 1 1 2 2 1 35
## 23.375 23.3846 23.3999 23.4166 23.4285 23.4347 23.4375 23.4444
## 4 1 10 1 5 1 1 1
## 23.4545 23.4722 23.4736 23.5 23.5454 23.5555 23.5714 23.5813
## 2 1 1 79 1 3 2 1
## 23.5882 23.6 23.6153 23.625 23.6363 23.6666 23.6923 23.6999
## 1 10 1 4 1 37 1 1
## 23.7142 23.7272 23.75 23.7777 23.8 23.8235 23.8333 23.8571
## 11 1 14 1 16 1 2 3
## 23.875 23.8888 23.8999 23.909 23.923 23.9499 24 24.0588
## 1 1 1 2 1 1 247 1
## 24.1 24.1052 24.1111 24.12 24.125 24.1428 24.1499 24.1666
## 1 1 4 1 2 3 1 3
## 24.1764 24.1999 24.2222 24.2307 24.25 24.2857 24.3076 24.3333
## 1 9 2 2 17 4 1 31
## 24.3636 24.375 24.3846 24.3913 24.3999 24.4285 24.4642 24.5
## 1 4 1 1 10 4 1 75
## 24.5357 24.5454 24.5714 24.5833 24.6 24.6153 24.625 24.6666
## 1 1 4 1 14 1 4 37
## 24.6818 24.6875 24.6999 24.7142 24.7272 24.75 24.7777 24.7857
## 1 1 1 2 1 16 1 1
## 24.8 24.8181 24.8333 24.8571 24.8666 24.8888 24.8999 24.92
## 9 1 9 3 1 2 4 1
## 24.9411 24.9642 25 25.0476 25.0833 25.1111 25.125 25.1333
## 1 1 236 1 1 1 2 2
## 25.1428 25.1666 25.1999 25.25 25.2727 25.2857 25.3 25.3076
## 3 5 8 7 1 3 2 1
## 25.3333 25.3636 25.375 25.3999 25.4285 25.4444 25.4615 25.5
## 36 1 2 12 8 3 3 72
## 25.5384 25.5555 25.5714 25.6 25.6111 25.6153 25.625 25.6538
## 2 2 6 9 2 1 7 1
## 25.6666 25.6875 25.6999 25.7142 25.75 25.7727 25.7777 25.7894
## 29 1 1 6 15 1 3 1
## 25.8 25.8125 25.8333 25.8571 25.875 25.8999 25.9268 25.9333
## 8 1 1 4 1 1 1 1
## 25.9375 25.9473 26 26.0625 26.1071 26.1111 26.1428 26.1538
## 1 1 229 1 1 1 6 1
## 26.1666 26.1818 26.1875 26.1999 26.2105 26.2307 26.2352 26.25
## 7 1 1 6 1 1 1 16
## 26.2857 26.3 26.3333 26.3636 26.375 26.3999 26.4285 26.4375
## 2 2 29 1 1 12 3 1
## 26.4444 26.4545 26.4583 26.4705 26.5 26.5555 26.5714 26.6
## 2 1 1 1 72 7 3 7
## 26.625 26.6666 26.6923 26.6999 26.75 26.7894 26.8 26.8333
## 3 31 1 1 17 1 6 2
## 26.8571 26.875 26.8888 26.9166 27 27.0666 27.0714 27.1
## 4 1 1 2 229 1 1 2
## 27.1111 27.1304 27.1428 27.1538 27.1666 27.1818 27.1999 27.2142
## 2 1 5 2 6 1 12 1
## 27.2222 27.25 27.2631 27.2857 27.3 27.3076 27.3333 27.3636
## 3 11 1 5 2 1 28 2
## 27.375 27.3999 27.4166 27.4285 27.4444 27.5 27.5333 27.5454
## 2 11 1 3 1 68 1 1
## 27.5555 27.5625 27.5714 27.6 27.625 27.6666 27.6818 27.6842
## 2 1 3 6 2 30 2 1
## 27.6999 27.7142 27.75 27.7692 27.8 27.8333 27.8571 27.8666
## 1 3 13 1 9 7 5 1
## 27.875 27.909 27.923 28 28.0909 28.1 28.125 28.1333
## 3 1 1 215 1 1 2 1
## 28.1428 28.1538 28.1666 28.1875 28.1999 28.2142 28.2222 28.25
## 3 1 7 1 10 1 1 16
## 28.2727 28.2857 28.3125 28.3181 28.3333 28.35 28.3636 28.375
## 1 7 1 1 34 1 2 1
## 28.3888 28.3999 28.4166 28.4285 28.4375 28.4444 28.5 28.5454
## 1 7 1 5 1 3 74 3
## 28.5555 28.5714 28.5833 28.5882 28.6 28.6363 28.6666 28.6999
## 2 5 2 1 8 1 27 2
## 28.7142 28.7272 28.75 28.7692 28.7857 28.8 28.8181 28.8333
## 3 1 18 1 1 7 2 5
## 28.8571 28.875 28.8823 28.8888 28.8999 28.909 28.9285 28.9333
## 4 1 1 1 1 1 1 1
## 29 29.0625 29.0869 29.0909 29.1 29.125 29.1304 29.1428
## 188 2 1 1 1 1 1 1
## 29.1666 29.1999 29.25 29.2608 29.2857 29.2941 29.3 29.3125
## 5 6 10 1 2 1 2 1
## 29.3333 29.375 29.3999 29.4166 29.4285 29.4444 29.4705 29.5
## 38 2 6 1 3 2 1 54
## 29.5555 29.5714 29.5833 29.5882 29.6 29.6111 29.625 29.6666
## 2 3 1 1 14 1 2 27
## 29.7142 29.75 29.7777 29.8 29.8125 29.8181 29.8333 29.8571
## 1 16 2 10 2 1 3 6
## 29.8888 29.8999 29.9166 29.923 30 30.0625 30.0909 30.125
## 1 2 1 1 216 1 1 3
## 30.1428 30.1666 30.1818 30.1999 30.25 30.2857 30.3 30.3333
## 3 5 1 6 10 5 1 32
## 30.375 30.3999 30.4166 30.421 30.4285 30.4444 30.4615 30.5
## 2 6 2 1 5 1 1 65
## 30.5384 30.5555 30.5714 30.6 30.625 30.6666 30.6875 30.7027
## 1 1 3 11 6 27 1 1
## 30.7142 30.7222 30.75 30.7692 30.7777 30.8 30.8333 30.8421
## 2 1 23 1 4 10 2 1
## 30.8571 30.875 30.8888 30.8947 30.8999 30.909 31 31.0833
## 1 1 3 1 3 1 197 1
## 31.0909 31.1111 31.125 31.1666 31.1818 31.1999 31.2272 31.25
## 1 1 3 3 1 11 1 17
## 31.2857 31.3 31.3333 31.3529 31.375 31.3999 31.4285 31.4444
## 1 2 31 1 2 11 5 1
## 31.4545 31.5 31.5384 31.5555 31.5714 31.5833 31.6 31.625
## 1 69 1 2 6 2 3 2
## 31.6666 31.6999 31.7142 31.75 31.7647 31.7777 31.7826 31.7894
## 28 1 3 17 1 1 1 1
## 31.8 31.8333 31.8571 31.875 31.8888 31.9166 32 32.0666
## 7 6 3 1 4 1 182 1
## 32.0833 32.0909 32.1 32.125 32.1428 32.1666 32.1818 32.2
## 1 3 3 1 2 3 2 8
## 32.2142 32.2222 32.2413 32.25 32.3333 32.375 32.3999 32.4166
## 1 1 1 12 28 2 8 1
## 32.4285 32.4375 32.4444 32.4615 32.5 32.5454 32.5714 32.5833
## 1 1 1 1 53 1 3 3
## 32.6 32.625 32.6363 32.6428 32.6666 32.6923 32.7 32.7142
## 5 2 2 1 37 2 1 3
## 32.7272 32.75 32.7999 32.8333 32.8461 32.8571 32.875 32.909
## 2 13 12 6 1 1 1 1
## 32.9166 32.923 32.9333 33 33.0666 33.0869 33.0909 33.1
## 2 1 1 150 1 1 1 1
## 33.125 33.1538 33.1666 33.2 33.25 33.2857 33.2999 33.3076
## 1 1 8 4 4 3 2 1
## 33.3333 33.3437 33.3529 33.375 33.3846 33.3913 33.3999 33.4285
## 24 1 1 1 2 1 4 1
## 33.4333 33.4444 33.4545 33.5 33.5454 33.5555 33.5714 33.5833
## 1 2 1 65 1 1 1 1
## 33.6 33.6363 33.6666 33.7142 33.7333 33.75 33.7777 33.7999
## 3 1 29 3 1 16 1 12
## 33.8181 33.8333 33.8571 33.8666 33.875 33.8846 33.8888 33.8999
## 2 7 1 1 2 1 1 1
## 33.909 34 34.0769 34.0799 34.0909 34.1111 34.125 34.1428
## 1 167 1 1 1 1 4 2
## 34.1538 34.1666 34.1818 34.2 34.2222 34.2307 34.2352 34.25
## 1 2 1 12 1 1 1 14
## 34.2666 34.2857 34.2999 34.3333 34.375 34.3999 34.4285 34.4375
## 1 5 1 23 1 13 7 1
## 34.4444 34.4615 34.5 34.5555 34.5714 34.5833 34.6 34.625
## 2 1 69 1 2 1 4 2
## 34.6666 34.7142 34.7272 34.7333 34.75 34.7619 34.7777 34.7999
## 21 1 1 1 5 1 5 8
## 34.8181 34.8333 34.8571 34.875 34.8888 34.8999 34.909 35
## 2 4 1 3 3 5 1 190
## 35.0769 35.0909 35.1052 35.1111 35.125 35.1428 35.1666 35.1818
## 1 1 1 1 2 6 2 1
## 35.2 35.25 35.2727 35.2857 35.3333 35.3636 35.375 35.3999
## 5 13 1 4 29 1 3 15
## 35.4285 35.4375 35.4444 35.4615 35.5 35.5263 35.5384 35.5555
## 3 1 2 1 51 1 1 1
## 35.5714 35.5833 35.6 35.6153 35.625 35.6666 35.7058 35.7142
## 6 1 6 1 2 27 1 7
## 35.7297 35.7333 35.75 35.7777 35.7999 35.8333 35.8571 35.875
## 1 1 16 1 5 1 3 1
## 35.8888 35.8999 35.9166 35.9285 36 36.0833 36.125 36.1428
## 1 1 1 1 199 1 2 3
## 36.1666 36.1764 36.1818 36.2 36.2222 36.25 36.2727 36.2857
## 5 1 2 6 2 16 2 2
## 36.2999 36.3333 36.3999 36.4166 36.4285 36.4444 36.4615 36.5
## 1 32 5 1 3 1 1 51
## 36.5555 36.5625 36.5714 36.5833 36.6 36.625 36.6363 36.6666
## 1 1 5 3 9 3 1 19
## 36.6818 36.7142 36.7272 36.75 36.7999 36.8095 36.8125 36.8333
## 1 2 1 13 10 1 1 5
## 36.8571 36.875 36.8888 36.9375 37 37.1 37.1034 37.1111
## 1 1 2 1 153 1 1 1
## 37.1428 37.1666 37.1818 37.2 37.2222 37.25 37.2666 37.2727
## 3 3 1 12 1 21 1 1
## 37.2857 37.3125 37.3333 37.375 37.3999 37.4285 37.4444 37.4545
## 6 1 27 3 13 4 2 2
## 37.4615 37.5 37.5333 37.5555 37.5714 37.5833 37.6 37.625
## 1 55 1 1 5 2 12 1
## 37.6428 37.6666 37.6875 37.7 37.7142 37.7272 37.75 37.7777
## 1 31 1 1 1 2 15 1
## 37.7999 37.8181 37.8333 37.8421 37.8571 37.8666 37.875 37.8888
## 6 2 5 1 3 1 4 4
## 37.9047 37.9166 37.923 38 38.0555 38.0714 38.0909 38.1
## 1 2 1 132 1 1 1 1
## 38.1111 38.1176 38.1428 38.1666 38.1818 38.2 38.2222 38.25
## 2 1 1 4 1 9 1 15
## 38.2857 38.3076 38.3333 38.3571 38.375 38.3999 38.4166 38.4285
## 3 1 20 1 2 12 1 2
## 38.4444 38.5 38.5555 38.5714 38.6 38.6153 38.625 38.6363
## 1 43 4 1 5 2 1 2
## 38.6428 38.6666 38.7 38.7142 38.7272 38.75 38.7777 38.7999
## 1 23 3 4 2 7 2 8
## 38.8148 38.8181 38.8333 38.8461 38.8571 38.875 38.8888 38.8999
## 1 2 1 1 4 1 1 1
## 38.9333 39 39.0909 39.1111 39.125 39.1428 39.1599 39.1666
## 1 169 1 2 1 4 1 8
## 39.1818 39.2 39.2222 39.238 39.25 39.2857 39.2999 39.3333
## 1 9 1 1 21 3 2 31
## 39.3999 39.4285 39.4444 39.4545 39.4615 39.5 39.5555 39.5714
## 9 4 2 1 1 54 2 2
## 39.6 39.619 39.625 39.6666 39.7142 39.75 39.7777 39.7999
## 7 1 1 23 5 13 1 10
## 39.8333 39.8571 39.875 39.909 39.9411 40 40.0769 40.0909
## 2 6 1 1 1 166 1 2
## 40.1 40.125 40.1428 40.1666 40.2 40.2142 40.25 40.2727
## 1 1 3 6 7 1 13 1
## 40.2857 40.3333 40.3636 40.375 40.3999 40.4166 40.4285 40.4444
## 5 29 1 3 8 1 3 2
## 40.4545 40.5 40.5714 40.5789 40.6 40.625 40.6285 40.6315
## 1 42 3 1 8 1 1 1
## 40.6363 40.6666 40.6923 40.75 40.7777 40.7857 40.7999 40.8333
## 2 17 1 13 2 1 3 3
## 40.8571 40.909 41 41.0526 41.0625 41.1111 41.1666 41.2
## 3 2 146 1 1 1 1 2
## 41.2222 41.2307 41.25 41.2727 41.2857 41.3333 41.3636 41.375
## 1 1 14 1 2 23 1 1
## 41.3999 41.4166 41.4285 41.4444 41.4615 41.5 41.5714 41.5833
## 17 1 3 3 4 49 2 1
## 41.6 41.6666 41.7142 41.75 41.7777 41.7857 41.7999 41.8181
## 9 26 3 11 2 1 9 1
## 41.8333 41.875 41.8888 41.909 41.9166 41.9473 42 42.0833
## 4 2 2 1 1 1 138 1
## 42.0869 42.0909 42.1111 42.1176 42.125 42.1428 42.1666 42.2
## 1 2 1 1 1 1 3 7
## 42.2068 42.2222 42.25 42.2777 42.2857 42.3125 42.3333 42.3571
## 1 1 11 1 1 1 24 1
## 42.3636 42.375 42.3999 42.4285 42.5 42.5454 42.5714 42.5833
## 3 2 3 4 39 1 2 1
## 42.6 42.625 42.6428 42.6666 42.7142 42.7272 42.75 42.7777
## 6 2 1 25 4 1 20 1
## 42.7999 42.8333 42.8571 42.8823 42.8999 42.909 42.9166 43
## 9 2 3 1 1 1 1 118
## 43.0499 43.0555 43.0909 43.1 43.1428 43.1666 43.1818 43.2
## 1 1 1 2 1 3 1 10
## 43.2307 43.25 43.2727 43.2857 43.3333 43.3999 43.4285 43.4444
## 2 12 1 1 29 7 2 1
## 43.5 43.5263 43.5714 43.5769 43.6 43.625 43.6666 43.7142
## 43 1 5 1 7 4 18 5
## 43.75 43.7999 43.8181 43.8333 43.8461 43.8571 43.875 43.8888
## 15 5 1 5 1 2 1 2
## 43.9166 44 44.0666 44.0833 44.1 44.1111 44.125 44.1333
## 1 129 1 1 2 1 1 1
## 44.1428 44.1666 44.2 44.25 44.2857 44.2999 44.3333 44.3636
## 3 2 9 8 3 2 24 3
## 44.375 44.3999 44.4285 44.4545 44.4615 44.4666 44.5 44.5333
## 1 6 6 2 1 2 49 1
## 44.5555 44.5714 44.6 44.625 44.6666 44.7142 44.7222 44.7272
## 1 3 3 2 18 3 1 1
## 44.75 44.7777 44.7857 44.7999 44.8333 44.8571 44.875 44.8888
## 8 1 1 11 3 3 3 3
## 44.8999 44.909 44.9166 45 45.0714 45.1111 45.1428 45.1666
## 1 1 1 117 1 1 3 7
## 45.2 45.2222 45.25 45.2777 45.2857 45.3076 45.3125 45.3333
## 3 1 9 1 5 1 1 23
## 45.3636 45.375 45.3999 45.4117 45.4285 45.45 45.4545 45.5
## 1 1 10 1 2 1 1 33
## 45.5555 45.5625 45.5833 45.6 45.6153 45.625 45.6428 45.6666
## 1 1 2 3 2 2 1 21
## 45.7142 45.7272 45.75 45.7777 45.7999 45.8125 45.8235 45.8333
## 1 1 12 1 11 1 1 4
## 45.8571 45.875 45.8888 45.8999 45.909 45.9285 46 46.0909
## 2 3 1 1 1 1 136 1
## 46.1111 46.125 46.1428 46.1538 46.1666 46.1818 46.2 46.2222
## 2 2 1 1 5 1 6 2
## 46.25 46.2857 46.3333 46.375 46.3999 46.421 46.4285 46.4375
## 21 3 23 3 17 1 1 1
## 46.4444 46.4545 46.4615 46.5 46.5454 46.5555 46.5714 46.6
## 3 2 1 47 1 2 4 7
## 46.625 46.6363 46.6666 46.6923 46.7142 46.75 46.7777 46.7999
## 2 3 30 1 6 13 1 7
## 46.8333 46.8571 46.875 46.8888 46.8999 46.909 47 47.0833
## 1 3 3 1 1 1 135 1
## 47.1111 47.1176 47.125 47.1428 47.1666 47.2 47.2222 47.25
## 1 1 2 4 4 10 2 8
## 47.2857 47.3333 47.375 47.3999 47.4186 47.4285 47.4444 47.4615
## 1 24 1 7 1 2 2 1
## 47.4666 47.5 47.5714 47.6 47.6363 47.6666 47.7 47.7058
## 1 40 5 5 2 20 1 1
## 47.7142 47.7272 47.7333 47.75 47.7999 47.8181 47.8333 47.8571
## 3 1 1 13 8 1 1 5
## 47.875 47.8888 48 48.1 48.1111 48.125 48.1428 48.1666
## 1 2 105 1 1 2 3 4
## 48.2 48.2222 48.2307 48.25 48.2857 48.3333 48.3999 48.4166
## 4 1 1 19 1 25 8 1
## 48.4285 48.4444 48.4545 48.5 48.5454 48.5555 48.5714 48.5833
## 2 1 1 29 1 2 3 2
## 48.6 48.6153 48.625 48.6666 48.7142 48.7272 48.75 48.7692
## 6 1 2 13 4 1 5 1
## 48.7777 48.7999 48.8235 48.8333 48.84 48.8461 48.875 48.8888
## 1 1 1 6 1 1 2 3
## 48.9166 48.9615 49 49.0769 49.1176 49.125 49.1428 49.1538
## 1 1 113 1 1 1 4 1
## 49.1666 49.1818 49.2 49.2142 49.25 49.2727 49.2857 49.2999
## 6 1 7 1 14 1 1 2
## 49.3333 49.3636 49.375 49.3999 49.4285 49.4375 49.4444 49.5
## 22 3 2 6 1 1 1 47
## 49.5714 49.6 49.6666 49.7142 49.7272 49.75 49.7999 49.8571
## 2 11 19 2 1 11 7 1
## 49.8888 49.8999 49.909 49.9166 49.923 50 50.0909 50.1
## 1 1 1 1 1 127 1 1
## 50.1428 50.1666 50.1818 50.2 50.2222 50.25 50.2666 50.2857
## 3 4 1 11 1 13 1 3
## 50.2999 50.3125 50.3333 50.375 50.3999 50.4285 50.4444 50.4666
## 1 1 22 2 4 1 1 1
## 50.5 50.5333 50.5454 50.5555 50.6 50.625 50.6363 50.6666
## 52 1 1 1 8 1 1 15
## 50.7142 50.7368 50.75 50.7714 50.7999 50.8333 50.8571 50.8888
## 3 1 9 1 4 4 1 1
## 50.8999 50.909 51 51.0833 51.125 51.1428 51.1666 51.1818
## 1 1 138 2 1 1 7 1
## 51.2 51.25 51.2857 51.3076 51.3333 51.3478 51.375 51.3999
## 2 15 5 1 22 1 1 1
## 51.421 51.4444 51.5 51.5714 51.5833 51.6 51.6666 51.6923
## 1 1 38 4 1 5 16 1
## 51.7 51.7142 51.7272 51.75 51.7777 51.7857 51.7999 51.8333
## 1 7 1 12 3 1 9 5
## 51.8571 51.875 51.8999 51.909 51.9166 52 52.0769 52.0909
## 3 3 2 1 1 121 1 1
## 52.1111 52.125 52.1428 52.1666 52.1818 52.2 52.2222 52.2307
## 2 1 3 2 1 6 1 1
## 52.25 52.2857 52.3333 52.375 52.3999 52.421 52.4285 52.4615
## 10 1 30 1 6 1 2 1
## 52.5 52.5217 52.5454 52.5714 52.6 52.6111 52.625 52.6363
## 32 1 1 4 8 1 1 1
## 52.6666 52.6818 52.7 52.7142 52.7272 52.75 52.7777 52.7999
## 32 1 1 2 1 10 1 7
## 52.8333 52.8571 52.875 52.8888 53 53.0555 53.1428 53.1499
## 3 4 1 1 121 1 4 1
## 53.1666 53.1764 53.1818 53.2 53.2222 53.2352 53.25 53.2727
## 2 1 1 4 1 1 12 2
## 53.2857 53.3076 53.3333 53.375 53.3999 53.4166 53.4285 53.4545
## 2 1 20 2 6 1 1 1
## 53.5 53.5555 53.5714 53.6 53.625 53.6363 53.647 53.6666
## 34 1 2 7 3 1 1 17
## 53.6923 53.75 53.7777 53.7999 53.8333 53.8461 53.8571 53.8666
## 1 11 3 5 5 1 3 1
## 53.875 53.8888 53.8999 53.909 53.9333 54 54.0909 54.1
## 1 1 1 1 1 105 1 1
## 54.125 54.1428 54.1666 54.2 54.25 54.2857 54.3333 54.375
## 1 3 4 3 14 1 30 2
## 54.3999 54.5 54.5333 54.5454 54.5714 54.5806 54.6 54.6666
## 2 45 1 1 3 1 8 20
## 54.6923 54.7 54.7142 54.75 54.7999 54.8125 54.8333 54.8461
## 1 2 4 4 10 1 3 1
## 54.8571 54.875 54.8999 54.9333 55 55.0909 55.1 55.1111
## 1 2 1 1 116 1 1 1
## 55.125 55.1428 55.1538 55.1666 55.1818 55.2 55.25 55.2857
## 3 7 1 6 1 5 9 2
## 55.3333 55.375 55.3999 55.4166 55.4285 55.4375 55.4444 55.5
## 21 3 4 2 4 2 1 34
## 55.5714 55.5833 55.6 55.625 55.6363 55.6428 55.6666 55.7142
## 2 1 3 1 3 1 21 4
## 55.75 55.7777 55.7999 55.8333 55.8571 55.875 55.9333 56
## 15 1 5 4 3 1 1 127
## 56.0799 56.1111 56.1666 56.2 56.25 56.2857 56.2999 56.3333
## 1 1 3 9 8 2 2 18
## 56.375 56.3999 56.4285 56.5 56.5555 56.5714 56.5833 56.6
## 3 5 2 38 2 4 1 16
## 56.625 56.6428 56.6666 56.6875 56.7058 56.7142 56.75 56.7777
## 2 1 22 1 1 4 11 1
## 56.7999 56.8235 56.8333 56.8461 56.8571 56.909 57 57.1
## 7 1 1 1 2 1 118 1
## 57.125 57.1428 57.1666 57.2 57.2142 57.25 57.2857 57.2999
## 1 1 2 8 1 9 1 1
## 57.3076 57.3333 57.375 57.3999 57.4285 57.4444 57.4545 57.5
## 1 21 3 6 2 1 1 42
## 57.5263 57.5454 57.5555 57.6 57.6666 57.7 57.7142 57.75
## 1 1 2 6 23 1 2 19
## 57.7777 57.7999 57.8333 57.8571 57.9166 57.9285 58 58.0909
## 1 2 5 2 1 1 101 1
## 58.0952 58.1 58.1111 58.125 58.1428 58.1666 58.2 58.25
## 1 2 2 1 4 3 3 9
## 58.2857 58.3333 58.3999 58.4545 58.5 58.5555 58.6 58.6666
## 1 26 7 2 41 2 3 22
## 58.6923 58.7 58.7142 58.75 58.7777 58.7999 58.8181 58.8333
## 1 2 1 9 1 6 2 7
## 58.8571 58.875 58.8888 58.9166 59 59.0769 59.1 59.1111
## 1 1 1 1 130 1 1 1
## 59.125 59.1428 59.1666 59.2 59.2142 59.2222 59.25 59.2857
## 3 2 3 8 1 1 6 2
## 59.3333 59.375 59.3999 59.4166 59.4285 59.4444 59.5 59.5416
## 31 2 7 1 1 1 43 1
## 59.5555 59.5714 59.6 59.6363 59.6666 59.7 59.7142 59.75
## 2 2 6 1 17 1 3 14
## 59.7999 59.8181 59.8333 59.8571 59.875 60 60.1111 60.1333
## 6 3 5 2 1 127 4 1
## 60.1428 60.1666 60.1818 60.2 60.2222 60.25 60.2857 60.3333
## 1 3 1 5 1 9 6 27
## 60.375 60.3846 60.3999 60.4285 60.4444 60.5 60.5454 60.5714
## 1 2 5 3 2 43 2 3
## 60.5833 60.6 60.625 60.6666 60.6923 60.7 60.7142 60.7272
## 1 5 4 13 1 2 3 1
## 60.75 60.7999 60.8181 60.8333 60.8571 60.875 61 61.0909
## 10 2 1 4 1 2 135 1
## 61.125 61.1428 61.1666 61.2 61.2222 61.25 61.2857 61.3333
## 2 2 3 6 1 14 1 18
## 61.35 61.3636 61.375 61.3999 61.4285 61.4444 61.5 61.5333
## 1 1 1 10 1 1 42 1
## 61.5454 61.5555 61.5714 61.6 61.6666 61.7142 61.7333 61.75
## 2 2 1 6 22 3 1 8
## 61.7999 61.8333 61.8571 61.8888 61.909 61.923 62 62.0909
## 8 6 1 2 1 2 116 1
## 62.1111 62.1176 62.1428 62.1666 62.2 62.25 62.2727 62.3333
## 2 1 3 3 9 8 1 18
## 62.375 62.3999 62.4285 62.4615 62.5 62.5555 62.5714 62.6
## 2 6 2 1 44 1 1 3
## 62.625 62.6666 62.6923 62.7058 62.7142 62.7272 62.75 62.7999
## 1 19 1 1 1 1 15 4
## 62.8125 62.8181 62.8235 62.8333 62.8571 62.8666 62.875 62.8888
## 1 1 1 1 1 1 1 1
## 62.923 63 63.0833 63.1 63.1111 63.125 63.1481 63.1666
## 1 139 1 1 2 3 1 2
## 63.2 63.2105 63.2222 63.25 63.2857 63.2999 63.3076 63.3333
## 6 1 1 12 4 2 1 19
## 63.375 63.3999 63.4285 63.4545 63.5 63.5454 63.5555 63.5714
## 3 4 1 3 46 1 1 3
## 63.6 63.625 63.6363 63.6666 63.7142 63.7272 63.75 63.7647
## 6 1 1 23 2 2 17 1
## 63.7999 63.8333 63.8571 63.875 63.9473 64 64.0909 64.1111
## 3 2 4 2 1 125 1 1
## 64.125 64.1428 64.1666 64.2 64.2222 64.25 64.2857 64.3333
## 2 1 3 10 2 11 4 21
## 64.375 64.4 64.4166 64.4285 64.5 64.5217 64.5714 64.5999
## 1 8 1 2 43 1 2 4
## 64.625 64.6666 64.7 64.7142 64.75 64.7999 64.8181 64.875
## 1 12 1 2 1 14 2 3
## 64.8888 65 65.0555 65.125 65.1428 65.1538 65.1666 65.2
## 2 111 1 1 2 1 2 6
## 65.25 65.2857 65.2999 65.3333 65.3636 65.375 65.4 65.4285
## 11 2 2 25 1 2 1 3
## 65.5 65.5714 65.5999 65.6428 65.6666 65.7142 65.75 65.7777
## 45 2 3 1 26 1 9 1
## 65.7999 65.8125 65.8181 65.8571 65.909 66 66.0344 66.0714
## 9 1 1 2 1 102 1 1
## 66.0769 66.0909 66.1111 66.125 66.1428 66.1666 66.2 66.25
## 1 1 1 1 1 4 2 10
## 66.2631 66.2999 66.3333 66.3636 66.3913 66.4 66.4285 66.5
## 1 1 22 1 1 6 2 48
## 66.5652 66.5714 66.5999 66.625 66.6666 66.7142 66.75 66.7999
## 1 1 3 1 9 3 13 8
## 66.8333 66.8571 66.875 66.8888 66.9 67 67.0769 67.0999
## 2 2 2 1 1 116 1 1
## 67.1111 67.125 67.1428 67.1666 67.2 67.2142 67.25 67.2666
## 1 3 1 2 7 1 15 1
## 67.2857 67.2999 67.3125 67.3333 67.3529 67.375 67.4 67.4117
## 3 1 1 25 1 1 3 1
## 67.4166 67.4285 67.4444 67.5 67.5555 67.5833 67.5999 67.625
## 1 2 1 44 1 1 4 1
## 67.6666 67.6923 67.7 67.7142 67.7272 67.75 67.7999 67.8333
## 18 1 1 3 1 9 6 2
## 67.8571 67.875 68 68.1428 68.1666 68.2 68.2105 68.2222
## 1 2 89 4 6 6 1 2
## 68.25 68.2727 68.2857 68.2999 68.3333 68.375 68.4 68.4285
## 10 1 2 2 14 1 1 1
## 68.4545 68.5 68.5555 68.5714 68.5999 68.625 68.6666 68.7142
## 1 51 1 2 4 1 22 1
## 68.75 68.7777 68.7999 68.8181 68.8333 68.8571 68.875 68.9285
## 4 1 3 1 1 2 1 1
## 68.9411 69 69.0909 69.0999 69.1111 69.1666 69.2 69.2222
## 1 117 1 1 2 1 3 1
## 69.25 69.2857 69.3125 69.3333 69.375 69.4 69.4285 69.5
## 5 2 1 23 3 5 5 40
## 69.5555 69.5714 69.5909 69.5999 69.625 69.6666 69.7142 69.75
## 2 3 1 6 1 24 1 10
## 69.7777 69.7999 69.8181 69.8333 69.8571 69.875 69.9 69.9166
## 1 3 1 1 1 1 1 1
## 70 70.0999 70.1111 70.125 70.1428 70.1666 70.2 70.2222
## 110 1 1 1 1 5 7 2
## 70.25 70.2857 70.2999 70.3333 70.3529 70.3846 70.4 70.5
## 5 6 1 22 2 1 5 42
## 70.5555 70.5999 70.6153 70.6315 70.6666 70.6923 70.75 70.7777
## 1 4 1 1 18 1 9 1
## 70.7999 70.8333 70.8571 70.875 70.8888 70.9 70.923 71
## 4 3 2 1 2 1 1 93
## 71.0454 71.0909 71.125 71.1428 71.1666 71.2 71.2142 71.25
## 1 1 1 3 3 8 1 6
## 71.2857 71.2903 71.3333 71.375 71.4 71.4285 71.4347 71.4444
## 1 1 17 2 7 2 1 1
## 71.4516 71.5 71.5714 71.5999 71.6153 71.625 71.6666 71.6875
## 1 32 1 3 1 1 22 1
## 71.7142 71.75 71.7999 71.8333 71.8571 71.875 71.8888 71.9
## 1 7 4 1 4 1 1 1
## 71.9285 71.9375 72 72.0909 72.0999 72.1111 72.125 72.1428
## 1 1 95 1 1 1 1 1
## 72.1666 72.2 72.25 72.2857 72.3333 72.3809 72.3846 72.4
## 2 4 7 5 16 1 1 2
## 72.4285 72.4444 72.5 72.5555 72.5714 72.5999 72.6666 72.6842
## 2 3 47 1 2 4 13 1
## 72.7 72.7142 72.7272 72.75 72.7999 72.8333 72.8571 72.9
## 2 1 1 8 4 3 3 1
## 72.9411 73 73.1111 73.125 73.1428 73.1666 73.2 73.25
## 1 96 1 3 3 4 3 10
## 73.2727 73.2857 73.3333 73.375 73.4 73.4545 73.5 73.5454
## 1 1 14 1 6 1 44 1
## 73.5999 73.6666 73.7 73.7142 73.75 73.7777 73.7999 73.8181
## 4 20 1 5 9 2 6 1
## 73.8333 73.8571 73.875 73.8888 74 74.0999 74.1111 74.125
## 2 1 2 1 95 1 2 1
## 74.1333 74.1428 74.1666 74.2 74.25 74.2857 74.3333 74.3571
## 1 1 3 9 9 3 23 1
## 74.375 74.4 74.4285 74.4444 74.5 74.5714 74.5833 74.5999
## 1 11 1 1 32 2 1 8
## 74.6428 74.6666 74.7142 74.75 74.7777 74.7999 74.8333 74.8888
## 1 19 4 9 1 6 3 1
## 75 75.1111 75.125 75.1666 75.2 75.25 75.2857 75.3333
## 107 1 2 3 5 10 2 27
## 75.3571 75.4 75.4285 75.4444 75.4545 75.5 75.5555 75.5999
## 1 7 1 1 1 40 1 5
## 75.6666 75.7142 75.75 75.7647 75.7777 75.7999 75.8235 75.8333
## 14 1 10 1 1 6 1 3
## 75.8571 76 76.125 76.1428 76.1666 76.2 76.2222 76.2352
## 2 74 3 4 4 7 1 1
## 76.25 76.2727 76.2857 76.2999 76.3333 76.375 76.4 76.4285
## 12 3 1 1 19 3 8 1
## 76.4444 76.5 76.5454 76.5555 76.5714 76.5833 76.5999 76.625
## 2 31 1 2 2 1 4 2
## 76.6666 76.7 76.7142 76.75 76.7857 76.7999 76.8333 76.8571
## 18 2 6 9 1 8 2 1
## 76.875 77 77.0833 77.125 77.1428 77.1666 77.2 77.2222
## 1 79 1 2 4 2 5 2
## 77.25 77.2857 77.3333 77.375 77.4 77.4166 77.4285 77.5
## 9 2 22 1 4 1 1 46
## 77.5555 77.5714 77.5999 77.6153 77.625 77.65 77.6666 77.7
## 3 1 5 2 1 1 16 3
## 77.7142 77.75 77.7999 77.8333 77.8571 77.875 77.923 78
## 3 7 7 2 1 2 1 74
## 78.125 78.1428 78.1666 78.2 78.25 78.2857 78.3333 78.375
## 3 2 2 6 15 3 25 1
## 78.4 78.4285 78.4444 78.5 78.5454 78.5714 78.5833 78.5999
## 2 1 1 44 1 2 1 7
## 78.625 78.6666 78.7142 78.75 78.8333 78.875 78.8888 79
## 1 16 4 12 2 1 1 75
## 79.0769 79.0909 79.0999 79.1111 79.125 79.1428 79.1666 79.2
## 1 1 1 1 2 3 3 6
## 79.2222 79.25 79.2666 79.2857 79.3333 79.4 79.4285 79.5
## 1 11 1 4 17 3 1 31
## 79.5384 79.5714 79.5999 79.625 79.6666 79.7142 79.75 79.7777
## 1 2 4 3 12 1 8 1
## 79.7857 79.7999 79.8333 79.8571 79.9 79.909 80 80.0769
## 1 6 2 1 1 1 70 1
## 80.125 80.1428 80.1666 80.2 80.2222 80.25 80.2857 80.2999
## 1 3 5 3 1 5 2 1
## 80.3076 80.3333 80.375 80.4 80.4285 80.4444 80.4615 80.5
## 1 13 2 6 1 1 1 49
## 80.5384 80.5714 80.5999 80.6666 80.7142 80.75 80.7777 80.7999
## 1 1 2 29 2 8 1 4
## 80.8333 80.8461 80.8823 81 81.125 81.1428 81.1538 81.1666
## 2 1 1 68 1 4 1 2
## 81.2 81.2142 81.2222 81.2352 81.25 81.2857 81.3333 81.375
## 7 1 1 1 11 2 22 1
## 81.4 81.4285 81.5 81.5454 81.5714 81.5999 81.6666 81.7
## 2 3 37 1 1 3 14 1
## 81.75 81.7777 81.7999 81.8333 81.8421 81.8571 81.9 81.9411
## 8 1 3 2 1 1 1 1
## 82 82.125 82.1428 82.1666 82.2 82.25 82.2727 82.2857
## 70 1 1 2 2 15 1 3
## 82.3333 82.375 82.4 82.4444 82.5 82.5454 82.5555 82.5714
## 24 1 5 2 36 4 1 2
## 82.5999 82.625 82.6666 82.7142 82.75 82.7999 82.826 82.8333
## 5 1 16 2 10 4 1 2
## 82.8571 82.875 83 83.0999 83.1428 83.1666 83.2 83.2222
## 3 1 91 1 1 1 1 1
## 83.25 83.2857 83.2999 83.3333 83.375 83.4 83.4285 83.4545
## 9 2 1 16 2 8 2 1
## 83.5 83.5454 83.5714 83.5999 83.6363 83.6666 83.7142 83.7272
## 29 2 2 4 1 27 1 1
## 83.75 83.7999 83.8333 83.8461 83.875 83.909 83.9166 84
## 10 3 4 1 2 1 1 80
## 84.0526 84.0714 84.0833 84.0909 84.1111 84.125 84.1538 84.1666
## 1 1 1 1 2 1 1 3
## 84.2 84.25 84.2727 84.2857 84.3333 84.4 84.4444 84.5
## 3 9 1 4 18 7 1 31
## 84.5714 84.5999 84.6666 84.7142 84.75 84.7692 84.7999 84.8333
## 3 6 21 2 7 1 7 2
## 84.8571 84.9 84.923 85 85.1111 85.1428 85.1666 85.2
## 1 1 1 68 1 2 4 8
## 85.2222 85.2307 85.25 85.2857 85.3333 85.3529 85.4 85.4285
## 1 1 10 2 14 1 2 1
## 85.4545 85.4666 85.5 85.5714 85.5833 85.5999 85.625 85.6666
## 1 2 27 3 1 7 4 13
## 85.7142 85.75 85.7777 85.7999 85.8333 85.8571 85.8666 85.875
## 1 12 1 3 2 2 1 4
## 85.8888 85.9 86 86.0909 86.1111 86.1428 86.1666 86.1818
## 1 1 84 1 1 2 2 1
## 86.2 86.2307 86.25 86.2727 86.3333 86.375 86.4 86.4285
## 4 1 9 1 25 2 9 2
## 86.5 86.5555 86.5714 86.5999 86.625 86.6315 86.6363 86.6666
## 35 1 3 6 2 1 1 16
## 86.7 86.7142 86.7333 86.75 86.7999 86.8333 86.8571 86.875
## 1 2 1 7 8 3 2 2
## 86.8888 86.9 86.9285 87 87.125 87.1428 87.2 87.25
## 2 1 1 73 1 2 3 11
## 87.2857 87.3333 87.3846 87.4 87.4285 87.4444 87.4545 87.5
## 2 8 1 4 1 1 3 33
## 87.5555 87.5714 87.5999 87.6666 87.7142 87.75 87.7692 87.7999
## 1 1 9 14 1 9 1 8
## 87.8333 87.8571 87.9 87.92 88 88.0769 88.1111 88.1428
## 3 1 1 1 72 1 1 2
## 88.1666 88.2 88.25 88.2857 88.3333 88.3846 88.4 88.4285
## 3 7 8 1 18 1 9 1
## 88.4444 88.5 88.5714 88.5999 88.6363 88.6666 88.7222 88.75
## 1 30 2 4 1 16 1 5
## 88.7777 88.7999 88.8333 88.909 89 89.0769 89.0999 89.125
## 3 5 4 1 83 1 1 2
## 89.1428 89.1666 89.2 89.2307 89.25 89.2857 89.2999 89.3076
## 1 2 2 1 7 1 1 1
## 89.3333 89.375 89.4 89.4285 89.4444 89.5 89.5454 89.5555
## 19 3 4 3 1 40 1 2
## 89.5714 89.5999 89.625 89.6666 89.7142 89.7272 89.7368 89.75
## 1 6 2 19 3 1 2 9
## 89.7999 89.8333 89.8571 89.875 89.909 89.9333 90 90.125
## 7 6 1 1 1 1 80 1
## 90.1428 90.1666 90.1818 90.2 90.25 90.2857 90.3333 90.3571
## 2 1 2 4 5 2 20 1
## 90.4 90.4285 90.4444 90.5 90.5714 90.5999 90.6153 90.6666
## 2 4 1 26 2 7 1 18
## 90.7 90.7142 90.7272 90.75 90.7999 90.8333 90.8571 90.875
## 2 4 2 12 5 1 3 1
## 90.8888 91 91.0999 91.1111 91.1428 91.1666 91.1818 91.2
## 2 75 1 2 1 1 1 4
## 91.25 91.2999 91.3333 91.375 91.4 91.4285 91.5 91.5238
## 7 1 18 6 5 2 31 1
## 91.5714 91.5999 91.625 91.6666 91.7142 91.75 91.7647 91.7777
## 1 11 1 15 1 7 1 1
## 91.7857 91.7999 91.8333 91.8571 91.8888 91.9 91.923 92
## 1 5 2 1 1 1 1 79
## 92.0999 92.1111 92.125 92.1428 92.1666 92.2 92.25 92.2857
## 2 1 1 1 4 1 7 1
## 92.2999 92.3333 92.375 92.4 92.4166 92.4285 92.5 92.5333
## 1 20 1 4 1 1 28 1
## 92.5999 92.6666 92.7083 92.7142 92.75 92.7777 92.7999 92.8181
## 8 16 1 3 7 2 2 1
## 92.8333 92.8571 92.875 92.8888 93 93.0833 93.1111 93.1428
## 2 4 1 1 84 1 1 3
## 93.1538 93.1666 93.2 93.25 93.2727 93.2857 93.2999 93.3333
## 1 3 8 7 1 2 1 17
## 93.4 93.4285 93.5 93.5454 93.5714 93.5999 93.625 93.6666
## 4 1 29 1 1 3 1 14
## 93.7142 93.7272 93.75 93.7999 93.8333 93.8666 93.8888 93.909
## 2 1 11 4 1 1 1 1
## 94 94.0999 94.1111 94.1428 94.1666 94.1818 94.2 94.25
## 83 2 1 1 1 1 4 14
## 94.2857 94.3333 94.3636 94.375 94.4 94.5 94.5555 94.5999
## 2 19 1 1 8 35 1 3
## 94.625 94.6666 94.7 94.75 94.7999 94.8333 94.8571 94.8888
## 1 15 1 7 3 3 4 1
## 94.9 94.909 95 95.0999 95.1111 95.125 95.1333 95.1428
## 1 1 80 2 2 1 1 3
## 95.1666 95.2 95.2142 95.2222 95.25 95.3333 95.4 95.4285
## 1 4 1 2 5 11 5 2
## 95.4444 95.5 95.5555 95.5714 95.5999 95.625 95.6363 95.6666
## 1 32 1 1 5 1 1 11
## 95.7142 95.75 95.7999 95.8499 95.8571 95.8823 96 96.1111
## 3 10 4 1 1 1 76 1
## 96.125 96.1428 96.1666 96.2 96.2222 96.2307 96.25 96.2857
## 1 1 2 2 1 2 7 2
## 96.3333 96.4 96.4545 96.4615 96.5 96.5714 96.5999 96.6363
## 19 2 1 1 27 3 7 1
## 96.6666 96.7142 96.75 96.7999 96.8333 96.8571 96.8888 97
## 8 1 9 9 2 1 2 82
## 97.1666 97.1818 97.2 97.25 97.3333 97.3846 97.4 97.4285
## 4 2 5 5 10 1 2 6
## 97.5 97.5714 97.625 97.6666 97.7142 97.75 97.7777 97.7999
## 32 1 1 10 1 6 1 5
## 97.8181 97.8333 97.8571 97.8888 97.909 97.9166 97.9285 98
## 1 1 2 1 1 1 1 71
## 98.0833 98.0999 98.1111 98.1666 98.2 98.2222 98.25 98.2857
## 1 1 1 4 6 1 9 1
## 98.3333 98.375 98.4 98.4285 98.5 98.5999 98.6363 98.6666
## 22 2 3 5 45 6 1 8
## 98.7142 98.75 98.7999 98.8333 98.8571 98.875 99 99.125
## 2 9 6 1 2 1 77 1
## 99.1428 99.1666 99.2 99.2307 99.25 99.2727 99.2857 99.2999
## 2 3 8 1 5 1 2 1
## 99.3199 99.3333 99.3636 99.4 99.4285 99.5 99.5714 99.5999
## 1 14 2 5 2 32 2 4
## 99.625 99.6363 99.6666 99.7142 99.7272 99.75 99.7692 99.7999
## 1 1 23 1 1 11 1 4
## 99.8333 99.8571 99.875 100 100.0999 100.1428 100.1666 100.2222
## 3 3 2 88 1 2 3 1
## 100.25 100.3333 100.4 100.4444 100.5 100.5555 100.5999 100.625
## 8 16 11 1 31 1 5 3
## 100.6666 100.7142 100.75 100.8 100.8333 100.8461 100.8571 100.8666
## 11 2 8 4 1 1 2 1
## 101 101.0909 101.1428 101.1666 101.2 101.2142 101.25 101.2857
## 83 1 2 3 5 1 8 3
## 101.3333 101.4 101.5 101.5384 101.5555 101.5833 101.5999 101.625
## 17 5 26 1 2 1 4 1
## 101.6666 101.7142 101.7272 101.75 101.7777 101.8 101.8571 101.909
## 17 2 1 4 1 7 1 2
## 102 102.0999 102.1428 102.1666 102.2 102.25 102.2857 102.3
## 80 1 1 7 2 9 2 1
## 102.3333 102.3636 102.375 102.4 102.4285 102.5 102.5714 102.5999
## 16 1 1 4 1 34 1 4
## 102.6666 102.6785 102.7142 102.75 102.8 102.8333 102.8571 102.9
## 16 1 1 12 6 5 2 1
## 102.909 102.9166 102.9375 103 103.125 103.1666 103.2 103.2222
## 1 1 1 90 1 2 6 1
## 103.25 103.2857 103.3333 103.375 103.4 103.4285 103.4444 103.5
## 15 1 15 3 3 1 2 31
## 103.5555 103.5714 103.5999 103.625 103.6666 103.6923 103.75 103.8
## 1 1 6 2 11 1 16 2
## 103.8181 103.8333 103.8571 103.875 103.909 104 104.0769 104.125
## 1 3 2 1 1 83 1 1
## 104.1428 104.1666 104.1904 104.2 104.2222 104.25 104.2857 104.3333
## 1 4 1 5 2 10 1 19
## 104.4 104.4285 104.4444 104.5 104.5999 104.6666 104.7222 104.75
## 7 1 1 28 3 12 1 3
## 104.7692 104.8 104.8333 104.8571 104.875 105 105.125 105.2
## 1 3 5 3 1 80 1 4
## 105.25 105.2857 105.3333 105.4 105.4444 105.5 105.5999 105.6666
## 12 1 17 2 1 34 6 10
## 105.75 105.8 105.8333 105.8571 105.875 105.9 106 106.0999
## 10 7 3 1 1 1 85 1
## 106.125 106.1428 106.1666 106.2 106.25 106.2857 106.3 106.3333
## 1 1 2 2 7 1 2 8
## 106.4 106.4285 106.4444 106.4545 106.5 106.5454 106.5555 106.5714
## 5 1 1 1 31 1 1 2
## 106.5999 106.6666 106.6875 106.6923 106.75 106.8 106.8666 106.9166
## 3 15 1 1 4 4 1 1
## 106.9523 107 107.0555 107.0909 107.1111 107.1428 107.1666 107.2
## 1 78 1 1 1 1 4 6
## 107.25 107.2857 107.3333 107.4 107.4444 107.4615 107.5 107.5714
## 5 1 18 3 1 1 26 1
## 107.5833 107.5999 107.625 107.6666 107.7333 107.75 107.8 107.8333
## 1 8 1 10 1 14 2 2
## 107.8571 108 108.0833 108.1428 108.2 108.2222 108.2307 108.25
## 2 96 1 1 6 1 1 7
## 108.2857 108.3 108.3333 108.375 108.4 108.4117 108.4285 108.5
## 3 1 17 2 2 1 3 23
## 108.5454 108.5714 108.5999 108.625 108.6363 108.6666 108.6875 108.7142
## 1 2 3 1 1 17 1 1
## 108.75 108.7777 108.8 108.8333 108.8571 109 109.0909 109.1333
## 12 1 5 6 2 106 1 1
## 109.1428 109.1666 109.2 109.25 109.3076 109.3333 109.375 109.4
## 2 1 4 6 1 11 2 3
## 109.5 109.5714 109.5999 109.6666 109.75 109.8 109.8333 109.8571
## 27 1 2 10 6 3 1 1
## 109.8695 109.9166 110 110.0833 110.125 110.1428 110.1666 110.2
## 1 1 89 1 1 2 4 5
## 110.2307 110.25 110.2857 110.3333 110.375 110.4 110.4285 110.5
## 2 6 1 15 2 6 2 18
## 110.5555 110.5714 110.5999 110.625 110.6666 110.7 110.75 110.8333
## 3 2 4 1 18 1 11 2
## 110.875 110.9 111 111.0999 111.1428 111.1666 111.1818 111.2
## 1 1 94 1 4 1 1 3
## 111.25 111.2857 111.3333 111.375 111.4 111.4444 111.5 111.5555
## 7 1 24 1 6 1 29 1
## 111.5714 111.5999 111.625 111.6666 111.7 111.75 111.8 111.8333
## 1 6 3 9 1 8 4 2
## 111.8461 111.875 111.8888 111.9 112 112.0833 112.1111 112.125
## 1 1 1 1 96 1 1 1
## 112.1428 112.1666 112.2 112.2222 112.25 112.2857 112.3333 112.3571
## 1 3 2 1 7 3 13 1
## 112.4 112.4285 112.5 112.5454 112.5714 112.5999 112.6666 112.7
## 1 1 33 1 1 3 11 2
## 112.7142 112.75 112.8 112.8333 112.8571 112.923 113 113.125
## 1 7 7 1 2 1 76 1
## 113.1428 113.1666 113.2 113.2222 113.25 113.2941 113.3333 113.375
## 2 3 2 1 5 1 14 2
## 113.4 113.4444 113.5 113.5333 113.5454 113.5999 113.6666 113.7142
## 3 1 28 1 1 3 20 1
## 113.7272 113.75 113.8333 113.8571 113.875 114 114.125 114.1428
## 1 7 4 2 1 82 1 2
## 114.1666 114.2 114.2142 114.25 114.2857 114.3333 114.3571 114.375
## 3 5 1 11 1 14 1 1
## 114.4 114.4285 114.45 114.5 114.5999 114.6666 114.7142 114.75
## 4 1 1 44 6 12 1 8
## 114.8 114.8333 114.8571 114.8636 114.875 114.923 115 115.1111
## 2 5 1 1 1 1 74 1
## 115.125 115.1428 115.1666 115.2 115.25 115.2857 115.3333 115.4
## 1 2 2 5 4 1 22 2
## 115.4166 115.4285 115.4545 115.5 115.5555 115.5714 115.5833 115.5999
## 1 3 1 28 2 1 1 2
## 115.6666 115.7142 115.75 115.7777 115.8 115.8333 115.8571 115.875
## 22 1 6 1 3 1 1 1
## 115.8888 116 116.0999 116.125 116.1333 116.1428 116.1666 116.2
## 1 94 1 1 1 1 1 4
## 116.25 116.2727 116.3 116.3333 116.4 116.4285 116.5 116.5555
## 6 1 1 15 4 1 28 1
## 116.5714 116.5833 116.5999 116.625 116.6666 116.7142 116.75 116.7777
## 2 1 2 3 12 1 4 1
## 116.8 116.8125 116.8333 116.8421 116.8571 117 117.0833 117.1111
## 3 1 4 1 1 69 1 2
## 117.1666 117.2 117.25 117.3 117.3076 117.3333 117.3636 117.375
## 2 4 5 4 1 15 1 1
## 117.4 117.4285 117.5 117.5714 117.5999 117.6666 117.7 117.75
## 2 1 33 2 2 9 1 6
## 117.8 117.8333 117.8571 117.875 117.909 117.9285 118 118.1111
## 1 3 2 1 1 1 89 1
## 118.1666 118.2 118.25 118.2857 118.3 118.3333 118.375 118.4
## 1 4 9 1 1 27 1 6
## 118.4444 118.5 118.5555 118.5714 118.5999 118.625 118.6666 118.7142
## 1 26 1 3 3 1 18 1
## 118.75 118.7777 118.8 118.8333 118.8571 118.875 119 119.125
## 3 1 6 3 2 2 91 1
## 119.1428 119.1666 119.2 119.25 119.3333 119.375 119.4 119.5
## 2 1 3 8 16 1 6 25
## 119.5555 119.5714 119.5999 119.6363 119.6666 119.7142 119.75 119.7857
## 1 1 5 1 9 1 4 1
## 119.8 119.8125 119.8571 119.875 119.8888 119.9166 120 120.0526
## 3 1 3 1 1 1 85 1
## 120.1428 120.1538 120.1666 120.2 120.25 120.2857 120.3333 120.4
## 2 1 3 5 5 1 13 5
## 120.4285 120.4444 120.5 120.5714 120.5999 120.625 120.6666 120.7142
## 3 1 24 3 2 1 8 2
## 120.7333 120.75 120.8 120.8571 121 121.125 121.1666 121.2
## 1 8 2 3 80 1 2 5
## 121.25 121.2857 121.3333 121.4 121.4444 121.5 121.5555 121.5999
## 10 1 19 1 1 18 1 4
## 121.6363 121.6666 121.7 121.7142 121.75 121.7777 121.8 121.8333
## 1 12 1 1 7 1 1 3
## 121.8499 121.875 121.8947 122 122.0666 122.0833 122.125 122.1428
## 1 2 1 82 2 1 2 1
## 122.1666 122.2 122.2222 122.25 122.2857 122.3333 122.4 122.4285
## 1 2 1 7 2 10 7 1
## 122.5 122.5714 122.5999 122.6666 122.7 122.7142 122.75 122.7647
## 27 1 1 13 1 1 7 1
## 122.7857 122.8 122.8333 122.8571 122.8888 122.9 123 123.0833
## 1 1 3 1 1 2 79 1
## 123.125 123.1428 123.1666 123.2 123.25 123.2857 123.3333 123.375
## 2 2 6 2 4 2 17 1
## 123.4 123.4285 123.5 123.5714 123.5833 123.5999 123.625 123.6666
## 5 1 22 3 1 2 2 10
## 123.75 123.7777 123.8 123.8333 123.8571 123.875 124 124.125
## 13 1 8 2 3 1 76 2
## 124.1428 124.1666 124.2 124.2222 124.25 124.2666 124.2857 124.3333
## 1 1 8 1 3 1 1 16
## 124.4 124.4545 124.5 124.5714 124.5833 124.5999 124.6363 124.6666
## 1 1 27 2 1 4 1 12
## 124.7142 124.7272 124.75 124.8 124.8333 124.8571 124.875 124.909
## 1 1 3 2 1 2 1 1
## 125 125.0769 125.0999 125.125 125.1428 125.1666 125.2 125.25
## 67 1 1 1 1 4 5 6
## 125.2857 125.3333 125.4 125.4615 125.5 125.5714 125.625 125.6363
## 3 10 8 1 21 1 1 1
## 125.6666 125.7142 125.75 125.8 125.8333 125.8571 125.8888 125.9166
## 10 3 9 3 1 3 1 2
## 126 126.125 126.1666 126.2 126.25 126.2857 126.3333 126.4
## 76 1 4 1 11 2 10 5
## 126.4285 126.5 126.5555 126.5714 126.5999 126.6666 126.75 126.8
## 1 16 1 2 4 21 5 5
## 126.8888 126.9 126.9333 127 127.0999 127.1666 127.25 127.2666
## 2 1 1 80 1 1 7 1
## 127.3333 127.375 127.4 127.4285 127.5 127.5714 127.5999 127.625
## 13 3 4 2 24 1 5 1
## 127.6666 127.7142 127.75 127.8 127.8333 127.8571 127.875 127.9
## 8 2 5 2 1 1 1 1
## 128 128.0909 128.125 128.1428 128.1666 128.1818 128.1999 128.25
## 82 1 1 1 1 1 5 6
## 128.2857 128.3333 128.4 128.4285 128.4444 128.5 128.5882 128.5999
## 1 9 1 1 1 19 2 2
## 128.625 128.6666 128.7142 128.7272 128.75 128.7777 128.8 128.8333
## 1 15 2 1 2 1 2 2
## 128.8571 128.875 128.9333 129 129.0769 129.0833 129.0999 129.1111
## 3 1 1 61 2 1 1 1
## 129.125 129.1428 129.1739 129.1818 129.1999 129.2222 129.25 129.2857
## 2 2 1 1 6 1 4 2
## 129.3333 129.4 129.4117 129.4285 129.5 129.5555 129.5714 129.5999
## 14 4 1 2 20 1 1 1
## 129.6666 129.6923 129.7272 129.75 129.7777 129.8 129.8333 129.8571
## 9 1 1 8 1 5 3 1
## 129.875 129.8888 130 130.125 130.1428 130.1818 130.1999 130.25
## 1 1 67 1 3 1 2 6
## 130.2857 130.3333 130.375 130.4 130.4444 130.5 130.5454 130.5999
## 2 9 1 3 1 17 1 3
## 130.6666 130.7142 130.75 130.7777 130.8 130.8333 130.8499 130.8571
## 12 1 6 1 2 2 1 1
## 130.9 131 131.1538 131.1666 131.1999 131.25 131.2941 131.3333
## 2 50 1 1 4 6 1 11
## 131.3636 131.4 131.4285 131.5 131.5454 131.5714 131.5999 131.6666
## 1 1 2 19 1 1 2 11
## 131.7142 131.75 131.7857 131.8 131.8333 132 132.0714 132.1111
## 1 7 1 3 1 56 1 1
## 132.125 132.1428 132.1666 132.1818 132.1999 132.25 132.2857 132.3
## 1 1 2 1 3 3 2 1
## 132.3333 132.375 132.4 132.4545 132.5 132.5833 132.5999 132.625
## 12 1 4 1 19 2 3 2
## 132.6666 132.7142 132.75 132.7916 132.8 132.8333 132.8571 132.8888
## 10 1 6 1 1 1 2 2
## 133 133.125 133.1666 133.1999 133.25 133.3 133.3333 133.375
## 80 2 2 3 5 1 10 1
## 133.4 133.4285 133.5 133.5714 133.5833 133.5999 133.6666 133.6923
## 2 3 20 1 1 2 10 1
## 133.75 133.7777 133.8 133.8333 133.8421 134 134.1428 134.1999
## 7 1 3 1 1 88 2 4
## 134.25 134.3333 134.4 134.4285 134.5 134.5555 134.5999 134.6666
## 9 12 2 1 26 1 2 12
## 134.75 134.7777 134.8 134.8333 134.8571 134.8888 134.9166 135
## 3 1 2 3 1 1 1 91
## 135.0769 135.1999 135.2222 135.25 135.3 135.3333 135.3529 135.3636
## 2 7 1 8 1 9 1 1
## 135.375 135.4 135.4285 135.5 135.5714 135.5999 135.6666 135.6999
## 1 4 1 25 1 3 10 1
## 135.75 135.8333 135.8571 136 136.1428 136.1666 136.1764 136.2222
## 5 3 3 59 1 1 1 1
## 136.25 136.2727 136.3333 136.375 136.4 136.4444 136.5 136.5714
## 7 1 14 2 2 2 25 2
## 136.5999 136.6666 136.6923 136.7142 136.75 136.8 136.8571 136.875
## 4 14 1 1 3 3 1 1
## 137 137.125 137.1428 137.1999 137.25 137.3333 137.4 137.4285
## 82 1 1 4 11 11 5 1
## 137.4444 137.5 137.5454 137.5999 137.625 137.6666 137.6999 137.75
## 2 17 1 2 2 10 1 5
## 137.8 137.8181 137.8333 137.875 137.909 137.9166 138 138.0999
## 5 2 3 3 2 1 61 1
## 138.125 138.1666 138.1999 138.25 138.2857 138.3333 138.4 138.4285
## 3 1 5 7 1 4 4 1
## 138.5 138.5294 138.5999 138.6666 138.75 138.7777 138.8 138.8333
## 22 1 5 11 2 1 2 2
## 138.909 139 139.1111 139.1428 139.1666 139.1999 139.25 139.2857
## 1 49 2 1 2 6 6 3
## 139.3333 139.4 139.4285 139.4444 139.5 139.5263 139.5714 139.5999
## 13 2 1 1 14 1 1 2
## 139.6666 139.7272 139.75 139.7777 139.8 139.8333 139.8571 140
## 8 1 9 1 1 1 2 65
## 140.1666 140.1999 140.25 140.3333 140.4 140.4285 140.5 140.5999
## 1 3 6 10 4 1 29 1
## 140.6666 140.6999 140.7142 140.75 140.8 140.8333 140.8571 141
## 14 1 3 6 6 1 1 56
## 141.125 141.1666 141.1999 141.25 141.2857 141.3333 141.4 141.4166
## 1 4 5 9 1 9 5 1
## 141.5 141.5999 141.625 141.6666 141.7142 141.7272 141.75 141.7777
## 14 2 1 8 1 1 7 1
## 141.8 141.8181 141.8571 142 142.1428 142.1666 142.1999 142.25
## 2 1 1 59 3 1 4 6
## 142.2857 142.3333 142.4 142.4285 142.5 142.5454 142.5999 142.625
## 1 14 5 1 25 1 5 1
## 142.6666 142.7142 142.75 142.8 142.8571 142.875 142.909 143
## 8 2 2 2 1 2 1 53
## 143.1428 143.1999 143.2222 143.25 143.3 143.3333 143.375 143.4
## 1 3 1 4 1 11 1 1
## 143.5 143.5333 143.5714 143.6666 143.6999 143.7142 143.75 143.7777
## 21 1 1 13 1 1 5 1
## 143.8 143.8333 143.9499 144 144.1111 144.1428 144.1666 144.1999
## 2 2 1 56 2 1 2 3
## 144.2222 144.25 144.2857 144.3333 144.375 144.4444 144.5 144.5714
## 2 7 2 11 1 1 19 1
## 144.5999 144.6666 144.75 144.8 144.8571 144.875 145 145.1428
## 3 5 4 3 1 1 58 2
## 145.1666 145.1999 145.2222 145.25 145.3 145.3333 145.375 145.4
## 4 5 1 4 1 3 2 6
## 145.4285 145.5 145.5999 145.625 145.6363 145.6666 145.6875 145.7272
## 2 15 2 1 1 8 1 1
## 145.75 145.8 145.8333 145.8571 145.8888 146 146.0909 146.1176
## 5 2 3 2 1 59 1 1
## 146.125 146.1428 146.1666 146.25 146.2857 146.3199 146.3333 146.4
## 1 1 4 6 1 1 9 3
## 146.5 146.5555 146.5625 146.5999 146.6666 146.6999 146.7272 146.75
## 16 1 1 2 10 1 1 8
## 146.8 146.8181 146.8333 147 147.125 147.1428 147.1666 147.1999
## 3 1 1 43 1 3 2 2
## 147.25 147.3333 147.4 147.5 147.5999 147.6666 147.7142 147.75
## 8 14 4 19 5 13 2 8
## 147.8 147.8571 147.8888 148 148.125 148.1428 148.1666 148.1818
## 2 2 1 53 1 2 1 1
## 148.1999 148.25 148.2857 148.3333 148.4 148.5 148.5999 148.6666
## 2 4 2 11 3 19 5 6
## 148.75 148.8 148.8333 148.8461 149 149.1666 149.1999 149.2222
## 5 2 2 1 68 3 1 1
## 149.25 149.3333 149.3636 149.375 149.5 149.5999 149.625 149.6666
## 4 8 1 1 12 6 2 1
## 149.7142 149.75 149.7894 149.8 149.8333 149.8888 149.9473 150
## 1 5 1 1 2 1 1 58
## 150.1428 150.1999 150.25 150.2857 150.3 150.3333 150.3636 150.4
## 1 3 3 1 1 12 1 1
## 150.4285 150.5 150.5999 150.6666 150.75 150.8 150.8333 150.8461
## 1 18 2 12 6 1 3 1
## 151 151.1538 151.1999 151.25 151.3333 151.4 151.4285 151.5
## 56 1 3 4 11 3 1 20
## 151.5555 151.5714 151.5999 151.6666 151.6999 151.75 151.8 151.8333
## 1 4 8 10 1 9 3 1
## 152 152.0909 152.1111 152.1999 152.2222 152.25 152.3333 152.4
## 54 1 1 4 1 1 6 1
## 152.4285 152.5 152.5714 152.5999 152.6666 152.6999 152.7142 152.75
## 1 16 1 1 5 1 1 2
## 152.7692 152.8 152.8333 152.875 153 153.125 153.1428 153.1666
## 1 1 1 1 48 1 1 1
## 153.1999 153.25 153.3333 153.375 153.4 153.4375 153.5 153.5714
## 4 5 6 1 3 1 27 1
## 153.5999 153.6666 153.75 153.8 153.8181 153.8571 154 154.1428
## 1 5 6 1 1 1 43 2
## 154.1666 154.1999 154.25 154.2857 154.3333 154.375 154.4 154.4285
## 3 2 3 1 10 4 2 1
## 154.5 154.5999 154.6666 154.75 154.8333 154.8571 154.8888 155
## 19 3 8 4 1 2 1 39
## 155.0769 155.0833 155.1428 155.1666 155.1999 155.25 155.2857 155.3333
## 1 1 1 3 2 8 2 14
## 155.4 155.5 155.5555 155.5999 155.6666 155.75 155.8 155.8333
## 2 17 1 3 11 8 1 1
## 155.8571 155.875 156 156.1666 156.1999 156.25 156.2857 156.3
## 1 1 53 1 1 9 1 1
## 156.3333 156.375 156.4 156.4166 156.4545 156.5 156.5999 156.6666
## 9 1 1 1 1 12 1 7
## 156.6999 156.7142 156.75 156.8333 157 157.1428 157.1666 157.1818
## 1 2 1 2 41 1 1 2
## 157.1999 157.25 157.3333 157.4 157.4285 157.4444 157.5 157.5999
## 3 1 9 1 1 1 16 2
## 157.625 157.6666 157.7142 157.75 157.8 157.8333 157.875 158
## 1 12 2 3 1 1 1 51
## 158.0833 158.1666 158.1999 158.25 158.2727 158.3 158.3333 158.375
## 1 2 1 4 2 1 17 1
## 158.4 158.4285 158.5 158.5714 158.5999 158.6666 158.7142 158.7272
## 3 1 19 1 1 5 2 1
## 158.75 158.8 158.8333 158.875 159 159.125 159.1428 159.1999
## 4 1 2 1 54 1 1 2
## 159.25 159.3333 159.4 159.5 159.5555 159.5714 159.5999 159.6666
## 9 10 3 10 4 2 5 7
## 159.75 159.8 159.8333 159.9285 160 160.0588 160.0714 160.1428
## 5 1 1 1 48 1 1 1
## 160.1666 160.1999 160.25 160.3333 160.3636 160.375 160.4 160.4285
## 2 3 2 5 1 1 4 1
## 160.5 160.5384 160.5714 160.5999 160.6666 160.75 160.8 160.8333
## 19 1 1 2 15 4 2 3
## 160.8571 161 161.1428 161.1666 161.1999 161.2222 161.25 161.2857
## 2 48 1 1 3 1 2 1
## 161.3333 161.375 161.4 161.4285 161.4444 161.5 161.5714 161.5999
## 11 1 2 1 1 15 1 2
## 161.6666 161.75 161.8 161.875 161.8888 162 162.0999 162.125
## 7 1 1 2 1 41 2 1
## 162.1666 162.1999 162.25 162.3333 162.4 162.5 162.5714 162.5999
## 2 2 4 15 4 17 1 3
## 162.647 162.6666 162.75 162.8333 162.9 163 163.1999 163.25
## 1 6 3 3 1 41 2 1
## 163.3333 163.375 163.4 163.4444 163.5 163.5714 163.6666 163.75
## 8 2 2 1 9 2 6 2
## 163.8 163.8333 164 164.125 164.1999 164.2222 164.2857 164.3333
## 1 3 51 1 2 1 1 5
## 164.4285 164.5 164.5555 164.5714 164.5999 164.6428 164.6666 164.7142
## 1 22 1 1 2 1 8 2
## 164.75 164.8 164.8333 164.8571 165 165.0999 165.1999 165.25
## 5 1 2 2 40 1 2 3
## 165.3333 165.4 165.5 165.5999 165.6666 165.7142 165.75 165.7777
## 4 1 15 2 3 1 3 1
## 165.8 165.909 166 166.125 166.1666 166.1999 166.25 166.3333
## 3 1 49 1 1 2 3 5
## 166.4 166.4444 166.5 166.5714 166.5999 166.6666 166.75 166.8571
## 1 1 17 1 1 8 7 1
## 167 167.125 167.1666 167.25 167.3333 167.4 167.4166 167.4285
## 50 1 2 1 10 1 1 2
## 167.5 167.5999 167.625 167.6666 167.75 167.8 167.8571 167.923
## 15 2 1 10 7 2 3 1
## 167.9545 168 168.0999 168.1999 168.25 168.3333 168.3636 168.4
## 1 53 2 2 2 6 1 1
## 168.4444 168.5 168.5999 168.6666 168.75 168.7777 168.8 168.8333
## 1 14 2 5 3 1 1 1
## 168.875 168.9 169 169.1666 169.25 169.3333 169.4 169.4285
## 1 1 53 3 3 5 2 1
## 169.4444 169.5 169.5555 169.5714 169.5999 169.6666 169.7142 169.75
## 1 12 1 1 6 5 1 6
## 169.8 169.8571 170 170.0555 170.0833 170.1428 170.1666 170.25
## 3 1 47 1 1 2 4 2
## 170.2857 170.3333 170.4 170.5 170.5555 170.5714 170.5999 170.6666
## 1 5 3 19 1 1 1 11
## 170.75 170.8 170.8333 170.8461 171 171.0999 171.1666 171.1999
## 4 2 1 1 41 1 1 6
## 171.25 171.2857 171.3333 171.4 171.4285 171.5 171.5999 171.6666
## 3 1 6 2 1 18 1 19
## 171.7272 171.75 171.8 171.8571 172 172.0909 172.1111 172.1999
## 1 6 2 1 51 1 1 2
## 172.25 172.2857 172.3333 172.3571 172.5 172.5833 172.5999 172.6666
## 3 1 1 1 16 1 3 9
## 172.75 172.8 172.8666 173 173.125 173.1666 173.2222 173.3333
## 3 1 1 37 1 1 1 6
## 173.3636 173.4 173.4285 173.5 173.5714 173.6666 173.75 173.8
## 1 3 1 15 1 4 4 2
## 173.8333 173.8571 173.875 174 174.125 174.1999 174.25 174.3333
## 1 3 1 36 2 2 6 5
## 174.375 174.4 174.4285 174.4444 174.5 174.5999 174.6666 174.7142
## 1 3 2 2 11 1 6 2
## 174.75 174.8 174.8333 175 175.1428 175.1666 175.1999 175.2222
## 4 2 4 37 1 1 1 1
## 175.25 175.3333 175.5 175.6666 175.7272 175.75 175.7894 175.8333
## 3 3 16 10 2 3 1 2
## 175.8571 175.8888 176 176.1666 176.1999 176.25 176.3333 176.3529
## 1 1 44 1 2 3 2 1
## 176.4 176.5 176.6666 176.8 176.8181 176.8571 177 177.1111
## 1 18 4 1 1 2 36 1
## 177.1428 177.1666 177.2857 177.3333 177.4 177.4705 177.5 177.5999
## 2 2 2 3 1 1 12 3
## 177.6666 177.7142 177.75 177.7692 177.8 177.8333 177.8571 177.875
## 9 1 2 1 3 1 1 1
## 178 178.125 178.1666 178.1999 178.25 178.3333 178.4 178.4285
## 30 1 1 3 4 3 2 1
## 178.5 178.5555 178.5714 178.5999 178.6666 178.7142 178.75 178.8333
## 18 2 1 2 2 1 3 1
## 178.9 179 179.125 179.1666 179.1999 179.25 179.3 179.3333
## 1 33 1 1 1 3 1 7
## 179.4 179.5 179.5999 179.6666 179.7142 179.75 179.8 179.8333
## 3 10 2 11 1 5 1 1
## 179.8571 180 180.125 180.1666 180.1999 180.25 180.3333 180.4
## 2 47 1 1 1 2 4 1
## 180.421 180.4285 180.5 180.5714 180.5999 180.625 180.6666 180.75
## 1 3 18 2 1 1 4 3
## 180.8 180.8333 180.875 181 181.1999 181.25 181.2857 181.3333
## 2 1 1 43 2 2 1 6
## 181.5 181.5555 181.5999 181.6666 181.75 181.8333 182 182.1428
## 12 1 2 5 2 1 38 3
## 182.1666 182.1999 182.2857 182.3333 182.4 182.5 182.5454 182.5999
## 3 1 1 8 2 15 1 1
## 182.6666 182.75 182.8 183 183.0952 183.1666 183.1999 183.25
## 6 5 3 52 1 1 3 2
## 183.2857 183.3333 183.4444 183.4545 183.5 183.5555 183.5714 183.5999
## 1 5 2 1 13 1 1 3
## 183.6666 183.75 183.8333 183.875 183.8947 183.9 183.909 184
## 8 3 1 2 1 1 1 35
## 184.0999 184.1999 184.25 184.2727 184.3333 184.4 184.5 184.5999
## 1 2 3 1 6 2 16 2
## 184.625 184.6666 184.75 184.8 184.8333 184.8571 185 185.0769
## 1 9 5 2 1 1 37 1
## 185.1428 185.1666 185.1999 185.25 185.3333 185.4 185.5 185.5454
## 1 1 3 1 4 1 22 1
## 185.5999 185.6153 185.6666 185.7333 185.75 185.8 185.8333 185.8571
## 1 1 4 1 3 2 1 1
## 186 186.1666 186.1999 186.25 186.3333 186.4 186.5 186.5714
## 41 2 1 2 4 2 16 1
## 186.625 186.6666 186.7142 186.75 186.7777 186.8333 186.8571 186.8888
## 1 3 1 4 1 1 1 1
## 187 187.1111 187.1666 187.1999 187.25 187.3333 187.375 187.4
## 37 2 3 2 3 4 1 1
## 187.5 187.5555 187.5714 187.6666 187.75 187.7777 187.8 187.8666
## 19 1 1 5 4 1 1 1
## 188 188.3333 188.375 188.4285 188.5 188.5999 188.6666 188.7333
## 42 2 1 2 14 1 5 2
## 188.75 188.8 188.8333 188.909 189 189.1666 189.1999 189.25
## 4 1 1 1 22 1 1 4
## 189.2857 189.3333 189.375 189.4 189.5 189.5714 189.5999 189.6666
## 1 5 1 2 11 1 1 6
## 189.75 189.875 190 190.1111 190.1428 190.1538 190.1666 190.1999
## 4 1 44 1 1 1 1 1
## 190.25 190.3333 190.4285 190.5 190.5999 190.625 190.6666 190.75
## 2 6 1 9 2 1 7 2
## 190.8 190.8333 190.8571 191 191.1999 191.2857 191.3333 191.5
## 2 1 1 32 1 1 5 13
## 191.5999 191.6666 191.7272 191.75 191.8 191.875 192 192.1666
## 3 5 1 3 2 1 47 1
## 192.1999 192.2222 192.25 192.3333 192.4166 192.5 192.5999 192.6666
## 4 1 3 5 1 14 2 10
## 192.75 192.8 192.8333 193 193.1538 193.1666 193.25 193.3333
## 4 1 1 41 1 1 2 4
## 193.375 193.4 193.5 193.6666 193.75 193.9166 194 194.25
## 1 2 13 2 1 1 45 3
## 194.2857 194.3333 194.375 194.4 194.4444 194.5 194.6666 194.7142
## 1 2 1 1 1 18 6 1
## 194.75 194.8 194.8571 195 195.25 195.3333 195.4 195.4285
## 3 4 2 35 4 3 1 1
## 195.5 195.5999 195.6666 195.75 195.8 195.8333 195.8571 195.909
## 10 1 2 2 2 1 1 1
## 196 196.1666 196.1999 196.25 196.3333 196.5 196.5999 196.6666
## 36 2 5 4 6 10 2 2
## 196.6999 196.7142 196.8 196.8571 196.8888 197 197.1666 197.1818
## 1 1 1 1 1 25 1 1
## 197.25 197.2857 197.3333 197.4 197.4444 197.5 197.5333 197.5714
## 2 1 11 1 1 18 1 1
## 197.6666 197.75 197.8 197.8333 197.8461 198 198.0999 198.1666
## 6 1 1 1 1 25 1 1
## 198.1999 198.3333 198.4 198.4166 198.4285 198.5 198.5333 198.5714
## 1 5 1 1 1 9 2 1
## 198.5999 198.6666 198.75 198.8 198.8333 198.9 199 199.1428
## 3 4 4 1 1 1 34 2
## 199.1999 199.25 199.3333 199.4 199.5 199.5999 199.625 199.6666
## 1 1 5 1 10 3 1 5
## 199.75 199.8 200 200.125 200.1428 200.1666 200.1999 200.25
## 3 1 28 1 2 2 2 4
## 200.3333 200.5 200.5999 200.6666 200.7142 200.75 200.7777 200.8333
## 5 11 3 7 1 2 1 2
## 201 201.1666 201.1999 201.25 201.3 201.3333 201.5 201.5999
## 35 1 1 4 1 3 20 1
## 201.6363 201.6666 201.75 201.8571 202 202.3333 202.5 202.6666
## 1 1 4 1 35 8 12 1
## 202.6999 202.75 202.8333 203 203.1666 203.1999 203.25 203.3
## 1 2 2 19 1 1 1 1
## 203.3333 203.5 203.5999 203.6666 203.75 203.7857 203.8 203.8333
## 4 14 2 5 1 1 1 1
## 203.8571 204 204.1999 204.25 204.3333 204.5 204.625 204.6666
## 1 40 1 2 1 11 1 8
## 204.75 204.8 204.909 205 205.1428 205.25 205.2857 205.3333
## 1 1 1 38 1 2 1 5
## 205.4 205.5 205.5999 205.6666 205.8 205.8333 206 206.1999
## 1 4 1 5 4 1 26 2
## 206.25 206.3333 206.4285 206.5 206.5714 206.5999 206.6666 206.7142
## 3 9 1 7 1 2 6 1
## 206.75 206.7777 206.8571 207 207.1428 207.1666 207.1999 207.25
## 4 1 1 31 1 1 1 4
## 207.2857 207.3333 207.4 207.5 207.5555 207.6666 207.8333 207.8571
## 1 2 1 9 1 3 1 1
## 207.875 208 208.1999 208.25 208.3333 208.5 208.5714 208.6666
## 1 23 1 3 2 7 1 1
## 208.6923 208.75 208.8 208.8571 208.8888 209 209.1538 209.1666
## 1 2 3 1 1 24 1 1
## 209.1999 209.25 209.3333 209.4 209.4285 209.5 209.5999 209.6666
## 1 1 3 2 1 10 1 6
## 209.75 209.8 209.8333 210 210.1666 210.25 210.2857 210.3333
## 4 2 1 27 1 2 1 2
## 210.5 210.6363 210.6666 210.75 210.8333 211 211.1999 211.25
## 13 1 5 3 1 33 2 2
## 211.2727 211.3 211.3333 211.4 211.5 211.5555 211.6666 211.75
## 1 1 7 3 13 1 3 3
## 212 212.125 212.1999 212.25 212.3333 212.5 212.5999 212.6666
## 34 1 1 4 3 9 1 5
## 212.7142 212.75 212.8 212.8571 213 213.0909 213.1666 213.25
## 2 2 2 1 29 1 1 3
## 213.3333 213.4 213.5 213.5714 213.6666 213.8 213.8333 213.8571
## 6 1 8 1 6 1 1 1
## 214 214.2857 214.3333 214.5 214.6666 214.6999 214.7142 214.75
## 20 1 5 12 3 1 1 2
## 214.8333 214.875 215 215.1999 215.25 215.3333 215.5 215.625
## 1 1 24 1 2 1 6 1
## 215.6666 215.7142 215.8 215.8333 216 216.125 216.1999 216.2857
## 4 1 1 2 42 1 4 1
## 216.3333 216.4285 216.5 216.5999 216.6666 216.75 216.8333 216.8571
## 11 1 10 1 3 3 1 1
## 217 217.25 217.3333 217.4 217.5 217.5999 217.6666 217.75
## 27 2 1 1 7 2 2 4
## 217.8333 218 218.3333 218.375 218.4285 218.5 218.6666 218.75
## 1 16 4 1 1 14 4 1
## 218.8 219 219.1999 219.3333 219.4 219.5 219.5999 219.6666
## 1 24 1 3 1 6 1 6
## 219.75 220 220.1428 220.1666 220.1999 220.2307 220.25 220.3333
## 2 30 2 1 1 1 4 1
## 220.5 220.5999 221 221.1999 221.25 221.3333 221.4 221.5
## 13 2 25 1 2 3 1 13
## 221.5999 221.6666 221.75 221.8 222 222.1111 222.25 222.3333
## 1 3 2 2 22 1 3 4
## 222.4 222.4444 222.5 222.6666 222.6999 222.7142 222.75 222.8888
## 3 1 7 3 1 1 1 1
## 223 223.1666 223.1999 223.25 223.3333 223.4166 223.5 223.6666
## 22 1 1 1 3 1 8 8
## 223.7142 223.75 224 224.125 224.1999 224.25 224.3333 224.5
## 1 2 23 1 1 2 3 11
## 224.5714 224.5999 224.6363 224.6666 224.7142 224.8888 225 225.1111
## 1 1 1 2 1 1 23 1
## 225.25 225.2857 225.3333 225.4 225.4285 225.5 225.5999 225.6666
## 1 1 2 3 1 9 2 3
## 225.8 226 226.1666 226.1999 226.3333 226.5 226.5714 226.5999
## 1 31 1 2 4 11 1 2
## 226.6666 226.6999 226.75 227 227.1666 227.25 227.2727 227.3333
## 2 1 1 21 1 3 1 2
## 227.4 227.5 227.5714 227.5999 227.6666 227.7142 227.75 227.8
## 3 8 1 1 4 1 2 1
## 227.909 228 228.1999 228.2857 228.3333 228.5 228.5999 228.6363
## 1 19 1 1 2 8 1 1
## 228.6666 228.75 228.8 228.8333 229 229.25 229.3333 229.4
## 4 1 1 2 21 3 3 1
## 229.5 229.6666 229.6999 229.8333 230 230.1666 230.1999 230.25
## 4 6 1 1 30 1 2 2
## 230.3333 230.5 230.5999 230.6666 230.75 230.8 231 231.125
## 3 8 1 2 4 1 14 1
## 231.1666 231.25 231.3333 231.5 231.6666 231.75 231.8 231.8571
## 2 2 1 4 4 1 1 1
## 232 232.1999 232.3333 232.4 232.4285 232.5 232.5714 232.5999
## 25 1 7 2 1 8 1 1
## 232.6666 232.75 232.8571 233 233.1666 233.3333 233.5 233.5999
## 4 1 1 16 1 3 10 2
## 233.6666 233.7142 233.75 233.8 234 234.2222 234.25 234.3333
## 6 1 1 1 22 2 1 2
## 234.4285 234.5 234.6666 234.75 234.8 235 235.1999 235.25
## 1 6 3 2 1 15 1 1
## 235.3333 235.4285 235.5 235.5999 235.625 235.6666 235.75 236
## 6 1 9 1 1 5 4 11
## 236.1666 236.2222 236.3333 236.5 236.5714 236.5999 236.6666 236.75
## 1 1 2 6 1 1 1 2
## 236.8333 236.9285 237 237.25 237.3333 237.4 237.5 237.5714
## 1 1 19 1 7 2 8 1
## 237.6666 237.75 237.8 237.875 238 238.1666 238.25 238.3333
## 2 2 1 1 11 1 3 3
## 238.375 238.5 238.6666 238.7777 238.8 239 239.25 239.3333
## 1 12 2 1 2 13 1 1
## 239.5 239.75 240 240.125 240.1666 240.1999 240.25 240.3333
## 6 1 26 1 1 1 1 2
## 240.4 240.5 240.5999 240.6666 240.7272 240.75 240.8571 241
## 2 8 1 6 1 1 1 29
## 241.25 241.3333 241.4 241.5 241.5999 241.6666 241.75 241.8
## 1 1 1 4 1 3 2 1
## 242 242.1428 242.25 242.3 242.3333 242.4 242.4444 242.5
## 28 1 1 1 1 3 1 6
## 242.5454 242.5999 242.6666 242.75 243 243.25 243.3333 243.5
## 1 2 4 6 21 1 3 14
## 243.5714 243.6666 243.75 243.909 244 244.1818 244.25 244.3
## 1 2 2 1 18 1 1 1
## 244.3333 244.4 244.4285 244.5 244.6666 244.75 244.8 244.8571
## 7 2 1 10 3 1 1 1
## 245 245.1666 245.25 245.3333 245.4285 245.5 245.5999 245.6666
## 18 1 2 5 1 7 1 4
## 245.8333 245.8571 246 246.1999 246.25 246.3333 246.4 246.4285
## 1 2 18 1 1 3 2 1
## 246.5 246.5172 246.6666 246.75 246.8 246.8333 247 247.0769
## 9 1 3 1 1 1 15 1
## 247.1428 247.1999 247.2222 247.25 247.3333 247.3636 247.4 247.5
## 1 1 1 3 2 1 1 6
## 247.6666 247.75 247.7894 247.8333 247.8571 247.9 248 248.125
## 3 1 1 2 1 1 16 1
## 248.1999 248.25 248.3333 248.375 248.4 248.5 248.6666 249
## 3 1 3 1 1 7 2 18
## 249.1666 249.3333 249.5 249.6666 249.75 249.8 250 250.1428
## 2 4 2 4 1 1 21 1
## 250.25 250.3333 250.375 250.5 250.5999 250.6666 250.8333 251
## 3 3 1 7 1 3 1 12
## 251.1999 251.3333 251.4 251.5 251.5999 251.6363 251.6666 252
## 1 2 2 9 1 1 1 16
## 252.1538 252.25 252.3333 252.5 252.5999 252.6666 252.75 252.7777
## 1 3 1 6 1 5 1 3
## 252.9 253 253.1666 253.25 253.3333 253.5 253.6666 253.8
## 1 27 1 2 2 5 2 1
## 254 254.25 254.3333 254.4 254.5 254.6666 254.75 254.8333
## 11 1 1 1 5 1 1 1
## 255 255.1666 255.1999 255.25 255.2857 255.3333 255.5 255.5714
## 20 2 1 1 1 5 7 1
## 255.5999 255.6666 255.75 256 256.1428 256.1999 256.3333 256.375
## 1 4 3 10 1 1 5 1
## 256.3999 256.5 256.6666 256.75 256.8333 257 257.1428 257.25
## 1 4 4 1 1 22 1 1
## 257.3333 257.5 257.6666 257.8 258 258.2857 258.3333 258.5
## 3 10 5 1 10 1 2 1
## 258.6 258.6666 259 259.25 259.3999 259.5 259.6666 259.75
## 2 1 14 2 2 12 1 3
## 259.8 260 260.1999 260.3333 260.3636 260.3999 260.5 260.5714
## 1 20 1 3 1 1 1 2
## 260.6666 260.8 260.8571 261 261.25 261.3333 261.5 261.6
## 1 1 1 15 2 8 3 1
## 261.6666 261.75 261.8571 262 262.3333 262.5 262.6666 262.8571
## 1 3 1 14 2 7 6 1
## 263 263.1666 263.25 263.3333 263.5 263.6666 264 264.25
## 21 1 1 5 4 1 10 1
## 264.2857 264.3333 264.375 264.3999 264.5 264.6666 264.75 265
## 2 2 1 1 3 1 1 20
## 265.1999 265.25 265.3333 265.5 265.7142 265.75 266 266.1999
## 1 1 1 5 2 1 17 1
## 266.25 266.3333 266.375 266.3999 266.5 266.6 266.6666 266.75
## 1 3 1 1 6 1 2 1
## 266.8 267 267.1999 267.25 267.3333 267.4285 267.5 267.6666
## 1 11 1 1 1 2 7 6
## 267.75 267.8333 268 268.1666 268.1999 268.25 268.3333 268.375
## 1 1 27 1 1 1 1 1
## 268.5 268.6 268.75 268.8333 269 269.3333 269.5 269.5384
## 3 2 1 1 20 3 1 1
## 269.6 269.6666 269.7142 269.75 269.8 270 270.3333 270.5
## 1 2 1 1 2 17 3 2
## 270.5833 270.625 270.6666 270.75 270.8 271 271.5 271.6
## 1 1 4 1 1 10 4 1
## 271.6666 272 272.25 272.3333 272.3999 272.5 272.6666 272.75
## 3 20 1 3 1 3 5 3
## 272.8571 273 273.3333 273.3999 273.5 273.6 273.625 273.6666
## 1 13 1 1 4 1 1 1
## 274 274.1111 274.1999 274.2857 274.3333 274.3999 274.4285 274.5
## 10 1 1 1 2 1 1 2
## 274.6 274.6666 274.75 275 275.1999 275.3333 275.5 275.75
## 1 4 2 15 1 2 1 3
## 275.8333 276 276.0909 276.1666 276.1999 276.5 276.6 277
## 2 23 1 1 1 4 1 16
## 277.25 277.3333 277.3999 277.5 277.6666 278 278.3333 278.5
## 2 2 1 2 1 14 3 3
## 278.6666 278.75 279 279.2222 279.3333 279.5 279.6666 279.75
## 1 1 14 1 1 6 1 1
## 280 280.25 280.3333 280.3999 280.5 280.75 281 281.3
## 9 1 3 1 4 2 13 1
## 281.5 281.6666 282 282.3333 282.3999 282.5 282.75 283
## 4 2 14 1 1 5 1 17
## 283.3333 283.3999 283.5 283.6666 283.8571 284 284.1666 284.25
## 1 1 5 1 1 16 1 1
## 284.3333 284.3999 284.4285 284.5 284.5555 285 285.3333 285.3999
## 1 1 1 2 1 15 1 1
## 285.5 285.6 286 286.3333 286.5 286.6666 286.75 287
## 2 1 16 1 5 1 1 13
## 287.25 287.3333 287.5 287.6666 287.8 288 288.25 288.5
## 3 1 3 3 1 19 3 3
## 288.6666 288.6999 288.75 288.8 289 289.25 289.3333 289.3846
## 4 1 1 1 20 3 1 1
## 289.5 289.6666 289.75 290 290.5 290.6666 290.7272 291
## 4 1 2 13 3 2 1 11
## 291.1428 291.25 291.3333 291.5 291.6666 291.8 292 292.3333
## 1 1 2 6 1 1 6 2
## 292.5 292.6666 292.75 293 293.3333 293.3999 293.5 293.6
## 3 1 2 6 1 1 3 1
## 293.6666 293.75 294 294.3333 294.5 294.6666 295 295.25
## 1 1 7 1 4 3 10 1
## 295.2727 295.3333 295.5 295.6 295.6666 295.7142 295.8 295.8571
## 1 2 2 1 1 1 1 1
## 296 296.1428 296.1999 296.25 296.3999 296.5 296.6666 296.75
## 11 1 1 1 1 4 2 2
## 297 297.5 297.6666 298 298.2222 298.3333 298.5 298.6666
## 15 2 3 8 1 4 1 1
## 298.8333 299 299.5 300 300.25 300.5 300.6 300.6666
## 2 14 4 9 1 3 1 3
## 301 301.1111 301.25 301.3999 301.5 301.6666 301.75 302
## 5 1 1 1 2 3 1 6
## 302.1999 302.3 302.3333 302.5 302.6666 302.8 303 303.3333
## 1 1 2 3 2 1 16 2
## 303.5 303.6666 303.8 304 304.1999 304.3333 304.3999 304.5
## 3 1 1 9 2 3 1 2
## 304.6666 304.8 305 305.5 305.6 306 306.3333 306.5
## 2 1 7 4 1 7 2 3
## 306.6666 306.75 307 307.3333 307.5 307.6666 307.75 307.8333
## 1 1 14 1 1 3 1 1
## 308 308.1666 308.3333 308.6666 309 309.3333 309.3999 309.5
## 7 1 1 1 8 5 1 1
## 309.6666 309.8 310 310.25 310.3333 310.5 310.75 311
## 1 1 4 1 2 4 1 6
## 311.25 311.3333 311.4285 311.5 311.6666 312 312.2857 312.6666
## 2 2 1 3 1 10 1 3
## 313 313.3333 313.5 313.75 314 314.3333 314.5 314.6
## 15 1 3 1 13 1 5 1
## 314.6666 314.75 315 315.5 316 316.5 316.6 316.6666
## 1 1 8 1 10 2 1 2
## 317 317.1666 317.3333 317.5 317.8 318 318.25 318.375
## 16 1 2 2 1 5 2 2
## 318.5 318.6666 318.909 319 319.25 319.3999 319.5 319.6666
## 4 1 1 14 1 1 5 4
## 320 320.25 320.3333 320.5 320.6666 321 321.1111 321.5
## 8 1 2 1 1 11 1 1
## 321.6666 322 322.25 322.5 322.8333 323 323.1428 323.25
## 1 10 3 2 1 7 1 1
## 323.5 323.75 324 324.5 325 325.25 325.5 325.75
## 3 2 6 4 20 1 4 1
## 326 326.5 326.6 326.6666 326.75 327 327.1999 327.8
## 8 4 1 1 1 11 1 1
## 328 328.5 329 329.3333 329.5 330 330.5 331
## 5 3 13 1 2 8 4 14
## 331.1666 331.3333 331.5 332 332.3999 332.5 332.8 332.8333
## 1 1 2 5 1 4 1 1
## 333 333.25 333.3333 333.5 333.6 333.6666 334 334.25
## 4 1 2 1 1 2 11 1
## 334.3333 334.5 334.6 334.6666 335 335.5 335.8333 336
## 1 1 1 1 3 3 1 4
## 336.25 336.5 336.6666 336.75 337 337.125 337.5 337.6666
## 1 1 1 1 5 1 2 2
## 337.75 338 338.5 339 339.5 339.6666 340 340.5
## 1 11 1 11 2 1 5 1
## 341 341.5 341.875 342 342.3333 343 343.5 343.6666
## 4 2 1 10 1 9 2 4
## 344 344.1999 344.25 344.3333 344.5 344.75 345 345.5
## 5 2 1 1 3 1 4 1
## 345.8333 346 346.25 346.3333 346.5 346.6666 347 347.409
## 1 3 3 1 2 1 11 1
## 347.5 348 348.6666 349 350 350.5 350.6666 350.75
## 2 11 2 8 9 2 1 1
## 351 351.25 351.5 352 352.5 352.6666 352.75 353
## 4 1 2 8 1 1 2 6
## 353.3333 353.6666 353.75 354 354.25 354.3999 354.5 354.75
## 2 3 1 7 1 1 3 1
## 354.8 355 355.1999 355.3333 355.5 355.6 356 356.3333
## 1 10 2 1 1 1 3 1
## 356.5 357 357.3333 357.3999 357.5 358 359 360
## 1 13 1 1 1 7 5 10
## 360.3333 360.5 360.6666 360.7142 361 361.25 361.3333 361.5
## 1 1 1 1 7 1 1 1
## 361.6666 361.7142 362 362.3333 362.5 363 363.5 364
## 1 2 11 2 3 4 1 6
## 364.1999 364.3333 364.5 365 365.1666 366 366.3333 366.5
## 1 1 2 8 1 10 1 2
## 367 367.1666 367.3999 367.6666 367.75 368 369 369.1666
## 7 1 1 1 1 7 7 1
## 369.25 369.75 370 370.25 370.3333 370.6666 371 371.5
## 1 1 2 1 1 1 3 1
## 371.75 372 373 373.1999 373.5 373.6666 374 374.1999
## 1 7 5 1 1 1 3 1
## 374.5 375 375.3333 375.3999 376 376.3333 376.625 376.6666
## 2 3 1 1 2 1 1 1
## 376.75 377 378 379 379.3333 380 380.5 380.75
## 1 5 2 3 1 6 2 1
## 381 381.5 381.8 382 382.3333 382.6666 383 383.5
## 7 1 1 4 1 1 8 1
## 384 384.5 385 385.25 385.5 386 386.3333 386.6666
## 4 1 4 1 1 6 1 2
## 387 387.3333 387.5 388 388.75 389 389.25 389.5
## 1 2 1 5 1 4 1 2
## 390 390.6666 391 391.5 392 392.3333 392.5 392.6666
## 3 2 5 3 3 1 2 1
## 393 393.6666 394 394.5 395.3333 395.6666 396 396.6666
## 8 1 1 1 2 1 5 1
## 397 397.5 398 399 400 400.5 401 401.3333
## 3 2 7 3 3 2 2 1
## 401.6666 402 402.6666 403 403.5 404 404.3333 404.6666
## 1 2 1 3 1 4 1 1
## 404.8333 405 405.1999 405.3333 405.5 406 407 407.5
## 2 2 1 1 1 1 3 2
## 408 408.6666 409 409.5 409.6666 409.8333 410 410.25
## 4 1 3 2 1 1 4 1
## 411 411.5 412 413 413.5 413.6666 414 414.6
## 1 1 2 2 2 1 4 1
## 415 415.5 416.5 417 417.6666 418 418.3333 418.5
## 3 1 2 2 2 1 1 2
## 418.6666 418.8 420 420.5 421 422.6666 423 423.3333
## 1 2 5 1 6 1 4 2
## 424 425 425.5 425.625 426 426.5 427 427.5
## 1 4 2 1 2 1 2 1
## 427.6666 428 428.5 428.75 429 430 431 431.5
## 1 7 1 1 1 2 3 1
## 432 433 433.5 434 434.5 435 436 436.5
## 2 2 1 2 2 1 3 1
## 437 438.5 440.5 441.5 441.75 442 443 444
## 3 1 2 1 1 1 3 2
## 445.5 446 447 448 448.3333 448.5 448.75 449
## 1 2 3 2 1 1 1 3
## 450.6666 451 451.3333 452 452.25 455 455.75 456
## 1 4 1 3 1 1 1 1
## 456.5 457 458 459 459.6666 460 461 461.4285
## 1 1 4 4 1 1 3 1
## 462.5 464 464.75 465 465.1666 465.5 466 466.6666
## 3 3 1 3 1 1 2 1
## 467 468 468.75 469 469.3333 470 470.6666 471
## 2 2 1 2 1 5 1 2
## 472 472.3333 473 474 475 476 477 478.5
## 2 1 2 2 2 1 2 1
## 480 480.3333 482 484 485 486 487 488
## 1 1 2 1 1 1 3 1
## 488.3999 489 489.3333 490 492 493.3333 495 496
## 1 2 1 5 1 1 1 3
## 497 498 499 502 504 508 509 512
## 1 1 1 1 1 1 2 2
## 513 516 516.6666 520 523 523.6666 525 527
## 2 1 1 1 2 1 1 1
## 528 528.6666 532 534 536 538 539 543
## 1 1 2 1 2 1 1 1
## 543.5 545 546 547 552 553 557 558
## 2 1 1 1 1 1 3 1
## 559 563 565 568 572 573 577 580
## 1 2 1 1 1 2 1 2
## 581 583 601 603 608 626 646 653
## 2 1 1 2 1 1 1 1
## 665 700 704
## 1 1 1here we observe that values are in a floating points.
finding the less than value 200 & 400.
NROW(data[which(data$x239<=200),])## [1] 58029here we observed 58% less than 200.
NROW(data[which(data$x239<=400),])## [1] 62920here we observed 62% are less than 400.
Now visualize the statistics
Histogram
hist(data$x239,
main = "Histogram of x239",
xlab = "x239")
abline(v =mean(data$x239, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 400 has max values.
Boxplot
boxplot(data$x239,
main = toupper("Boxplot of x239"),
ylab = "x239",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x239, na.rm = TRUE)
plot(d, main = "Kernel density of x239")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x239, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is 36% of missing values most of the values lies in b0 to 400 and the skewness is a postive because the tail will have the large values.so he variable has descrete values.
summary of the variable x240
summary(data$x240)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.178 1.000 44.000observe that values from 0 to 44, median is 0 & mean is 1.178
Description of the variable x240
describe(data$x240)## data$x240
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 36 0.8 1.178 1.788 0 0
## .25 .50 .75 .90 .95
## 0 0 1 4 6
##
## lowest : 0 1 2 3 4, highest: 32 33 34 38 44Here the kurtosis is positive and it has large data in tails.
Total number of values excluding NA
NROW(data$x240) - NROW(data[which(is.na(data$x240)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x240)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 57847 17610 8830 5105 3445 2138 1547 1033 697 471 362 248
## 12 13 14 15 16 17 18 19 20 21 22 23
## 165 143 79 66 62 37 23 22 15 10 9 6
## 24 25 26 27 28 29 30 32 33 34 38 44
## 9 1 4 2 2 2 5 1 1 1 1 1here we observe that 57 % of values are 0.
finding the less than value 1 & 7
NROW(data[which(data$x240<=1),])## [1] 75457here we observed 75% less than 1
NROW(data[which(data$x240<=7),])## [1] 97555here we observed 97% are less than 7
Now visualize the statistics
Histogram
hist(data$x240,
main = "Histogram of x240",
xlab = "x240")
abline(v =mean(data$x240, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10 has max values.
Boxplot
boxplot(data$x240,
main = toupper("Boxplot of x240"),
ylab = "x240",
col = "red")
## we found the outliers.
Density Plot
d <- density(data$x240, na.rm = TRUE)
plot(d, main = "Kernel density of x240")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x240, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 has more density there is no missing values most of the values are lies in b0 to 10 and the skewness is a postive because the tail will have the large values.so the variable has descrete values.
summary of the variable x241
summary(data$x241)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1121 0.0000 29.0000observe that values from 0 to 29, median is 0 & mean is 0.1121
Description of the variable x241
describe(data$x241)## data$x241
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 18 0.191 0.1121 0.2137 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 1
##
## Value 0 1 2 3 4 5 6 7 8 9
## Frequency 93190 4618 1252 426 230 113 70 38 21 12
## Proportion 0.932 0.046 0.013 0.004 0.002 0.001 0.001 0.000 0.000 0.000
##
## Value 10 11 12 13 14 15 17 29
## Frequency 13 6 3 1 3 2 1 1
## Proportion 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Here the kurtosis is positive and its has large data in tails.
Total number of values excluding NA
NROW(data$x241) - NROW(data[which(is.na(data$x241)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x241)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 93190 4618 1252 426 230 113 70 38 21 12 13 6
## 12 13 14 15 17 29
## 3 1 3 2 1 1here we observe that 93% of values are 0.
finding the less than value 1 & 3
NROW(data[which(data$x241<=1),])## [1] 97808here we observed 97% less than 1
NROW(data[which(data$x241<=3),])## [1] 99486here we observed 99% are less than 3
Now visualize the statistics
Histogram
hist(data$x241,
main = "Histogram of x241",
xlab = "x241")
abline(v =mean(data$x241, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 5 has max values.
Boxplot
boxplot(data$x241,
main = toupper("Boxplot of x241"),
ylab = "x241",
col = "red")
## we got the outliers & its shows like a line because medain & quartiles aree zero.
Density Plot
d <- density(data$x241, na.rm = TRUE)
plot(d, main = "Kernel density of x241")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x241, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here 0 has more density there is no missing values most of the values are lies in b0 to 3 and the skewness is a postive because the tail will have the large values.so the variable has descrete values.
summary of the variable x242
summary(data$x242)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.98 1.17 1.09 1.33 8.55 93339observe that values from 0 to 8, median is 1.17 & mean is 1.09, missing values ar 93%
Description of the variable x242
describe(data$x242)## data$x242
## n missing distinct Info Mean Gmd .05 .10
## 6661 93339 3288 1 1.091 0.4837 0.0010 0.3069
## .25 .50 .75 .90 .95
## 0.9828 1.1699 1.3266 1.4733 1.6100
##
## lowest : 0.0000 0.0008 0.0010 0.0011 0.0016, highest: 5.9266 6.5999 7.2999 7.5819 8.5533Here the kurtosis is positive and its has a large data in tails.
Total number of values excluding NA
NROW(data$x242) - NROW(data[which(is.na(data$x242)),])## [1] 6661Here the value is 6661 because there is 93% of missing values.
table(data$x242)##
## 0 8e-04 0.001 0.0011 0.0016 0.0023 0.0026 0.003 0.0032 0.0034
## 332 1 1 1 1 1 1 1 1 1
## 0.004 0.0061 0.0073 0.0078 0.008 0.0083 0.0085 0.0094 0.0099 0.01
## 1 1 1 1 1 1 1 1 1 1
## 0.0101 0.0107 0.0109 0.011 0.0112 0.0122 0.0125 0.0126 0.0132 0.0133
## 1 1 1 1 1 2 2 1 1 1
## 0.0138 0.0151 0.0156 0.0177 0.0179 0.0185 0.0194 0.0216 0.0226 0.025
## 1 2 1 2 2 1 1 1 1 1
## 0.0281 0.0282 0.0285 0.0295 0.0299 0.0306 0.0322 0.0325 0.0326 0.0341
## 1 1 1 2 2 3 1 1 1 2
## 0.0343 0.0346 0.0352 0.0354 0.0357 0.0359 0.0361 0.0365 0.0377 0.0381
## 2 1 1 1 1 1 1 1 1 1
## 0.0383 0.0386 0.039 0.0397 0.04 0.0401 0.0403 0.0407 0.041 0.0415
## 2 1 1 1 1 1 1 2 1 1
## 0.0429 0.0431 0.0433 0.0443 0.0444 0.0446 0.0454 0.0459 0.0463 0.0465
## 1 1 1 1 1 1 1 1 1 1
## 0.0466 0.0472 0.0495 0.05 0.051 0.0516 0.0529 0.0531 0.0534 0.0546
## 1 1 1 2 1 1 1 1 2 1
## 0.055 0.0556 0.056 0.0561 0.0566 0.0576 0.0578 0.0585 0.0589 0.0597
## 1 1 1 1 1 1 1 1 3 1
## 0.0599 0.0644 0.0645 0.0646 0.065 0.0656 0.067 0.0687 0.07 0.0705
## 2 1 1 1 1 1 1 2 4 1
## 0.0727 0.0729 0.074 0.0746 0.0756 0.0759 0.0762 0.0771 0.0777 0.0792
## 1 1 1 1 1 1 1 2 1 1
## 0.0815 0.0817 0.0818 0.083 0.0839 0.0852 0.0879 0.0893 0.0899 0.0904
## 1 1 1 1 1 1 1 1 1 1
## 0.0906 0.0907 0.0909 0.0919 0.0928 0.0943 0.095 0.0953 0.096 0.097
## 1 1 1 1 1 1 2 1 1 1
## 0.0982 0.0984 0.0987 0.1018 0.1026 0.103 0.1033 0.104 0.1048 0.1062
## 1 1 1 1 1 1 2 1 2 1
## 0.1066 0.1075 0.108 0.109 0.1094 0.11 0.1125 0.114 0.1226 0.124
## 2 1 1 1 1 3 1 1 1 1
## 0.125 0.126 0.1288 0.13 0.1325 0.134 0.1349 0.1351 0.1358 0.1361
## 1 2 1 2 1 1 1 1 1 1
## 0.1366 0.1394 0.1426 0.1432 0.1433 0.1439 0.1449 0.1466 0.1479 0.1499
## 1 1 1 1 1 1 1 1 1 2
## 0.1519 0.1557 0.157 0.158 0.1587 0.1588 0.1596 0.163 0.1633 0.1634
## 1 1 1 1 1 1 1 1 1 1
## 0.168 0.1683 0.169 0.1717 0.1729 0.1733 0.1738 0.1779 0.1809 0.1835
## 1 1 1 1 1 1 1 1 2 1
## 0.184 0.1868 0.191 0.194 0.1948 0.196 0.1967 0.1969 0.198 0.1982
## 1 1 1 1 1 2 1 1 1 1
## 0.1983 0.1997 0.2049 0.2055 0.2059 0.2079 0.2084 0.2091 0.2099 0.2113
## 1 1 1 1 1 1 1 1 1 1
## 0.2114 0.2115 0.2119 0.2124 0.2133 0.218 0.2199 0.22 0.2204 0.2207
## 2 1 1 1 2 1 1 1 1 1
## 0.2219 0.222 0.2232 0.2239 0.2243 0.227 0.2292 0.2293 0.23 0.232
## 1 1 1 1 1 1 1 1 1 1
## 0.2321 0.2326 0.2331 0.2348 0.2399 0.2403 0.2416 0.2419 0.2434 0.2439
## 2 1 1 1 1 1 1 1 1 2
## 0.2464 0.2466 0.2475 0.2486 0.2528 0.2533 0.254 0.2542 0.2561 0.2581
## 1 1 1 1 1 1 2 1 1 1
## 0.264 0.2647 0.265 0.267 0.2687 0.2707 0.2714 0.2716 0.272 0.273
## 1 1 1 1 1 1 1 1 1 1
## 0.275 0.2766 0.28 0.2834 0.2869 0.2879 0.2914 0.2921 0.2922 0.2925
## 2 1 1 1 1 2 1 1 1 1
## 0.2941 0.2949 0.2954 0.2959 0.3038 0.3044 0.3048 0.3049 0.3064 0.3066
## 1 2 2 1 1 1 1 1 3 1
## 0.3069 0.307 0.3081 0.3084 0.3092 0.31 0.3116 0.3117 0.312 0.3125
## 1 1 1 1 1 1 1 1 1 1
## 0.3133 0.314 0.3148 0.316 0.32 0.321 0.322 0.3225 0.3235 0.324
## 1 1 1 1 1 1 1 1 1 1
## 0.3256 0.3266 0.3295 0.33 0.3314 0.3333 0.3335 0.335 0.336 0.3366
## 1 2 1 1 1 1 1 1 1 1
## 0.337 0.3373 0.3375 0.34 0.3433 0.3439 0.3457 0.3466 0.3469 0.3479
## 1 1 1 1 1 2 2 2 1 1
## 0.3492 0.3499 0.3518 0.3533 0.3549 0.3556 0.3563 0.3574 0.3598 0.3599
## 1 1 1 1 1 1 1 1 1 1
## 0.3627 0.3634 0.3639 0.3649 0.3666 0.3674 0.3704 0.3712 0.3724 0.3725
## 1 1 1 1 1 1 3 1 1 1
## 0.3738 0.374 0.376 0.3761 0.3766 0.3774 0.3776 0.38 0.3853 0.386
## 1 1 1 1 1 1 1 1 1 1
## 0.3866 0.388 0.3881 0.3886 0.39 0.3911 0.3915 0.3991 0.4 0.4006
## 1 2 1 1 2 2 1 1 2 1
## 0.4026 0.406 0.4066 0.4079 0.4089 0.4128 0.4151 0.4153 0.4159 0.4182
## 1 1 1 1 1 1 1 1 1 1
## 0.4199 0.4233 0.4247 0.4263 0.4299 0.4314 0.4316 0.432 0.4333 0.435
## 2 1 1 1 2 1 2 1 1 1
## 0.4351 0.4353 0.4362 0.4371 0.438 0.44 0.445 0.4458 0.446 0.4466
## 1 1 1 1 1 1 1 1 1 1
## 0.447 0.4485 0.449 0.45 0.4517 0.4537 0.4538 0.4563 0.4566 0.4573
## 1 1 1 1 1 1 1 1 2 1
## 0.4575 0.4587 0.4588 0.46 0.4633 0.4663 0.4666 0.4699 0.4719 0.4727
## 1 1 1 1 2 1 1 1 2 1
## 0.4749 0.4756 0.4763 0.4776 0.4779 0.4783 0.4788 0.4794 0.4799 0.4805
## 1 1 1 1 1 1 1 1 2 1
## 0.4833 0.4848 0.4871 0.4874 0.4875 0.4879 0.4899 0.4919 0.4939 0.495
## 1 1 1 2 1 1 2 1 1 1
## 0.5004 0.5017 0.502 0.5041 0.5058 0.5066 0.5074 0.508 0.5086 0.51
## 1 1 1 1 1 3 1 1 1 1
## 0.514 0.5166 0.518 0.5187 0.5206 0.5223 0.5228 0.5229 0.525 0.5257
## 1 2 1 1 1 1 1 1 1 1
## 0.528 0.529 0.53 0.532 0.533 0.5353 0.5383 0.5391 0.54 0.542
## 1 1 3 1 1 1 1 1 3 2
## 0.5424 0.545 0.5451 0.5464 0.5468 0.5485 0.552 0.5524 0.5533 0.555
## 1 1 1 1 2 2 1 1 1 3
## 0.5566 0.5591 0.56 0.561 0.5612 0.562 0.5633 0.5645 0.5659 0.5665
## 1 1 1 1 1 1 1 1 1 1
## 0.5696 0.5699 0.5703 0.5723 0.5761 0.5775 0.5799 0.5839 0.585 0.5862
## 1 2 1 1 1 1 1 2 1 1
## 0.5866 0.5873 0.5893 0.5899 0.5917 0.5922 0.5933 0.5953 0.5959 0.5966
## 1 1 2 3 1 1 1 1 3 1
## 0.5973 0.5979 0.5981 0.5989 0.5999 0.6014 0.6019 0.6028 0.6043 0.6049
## 1 1 1 1 1 1 1 1 1 1
## 0.6059 0.6069 0.6079 0.6109 0.6116 0.6125 0.6126 0.613 0.6133 0.6146
## 1 1 1 1 1 1 1 1 1 1
## 0.6158 0.6179 0.6194 0.6197 0.62 0.622 0.624 0.6256 0.6257 0.626
## 1 1 1 1 4 1 1 1 1 1
## 0.6283 0.6325 0.6337 0.6358 0.638 0.6388 0.64 0.6425 0.6437 0.6467
## 1 1 3 1 1 1 3 1 1 1
## 0.65 0.6505 0.652 0.6541 0.66 0.662 0.6633 0.664 0.6641 0.6657
## 3 1 1 1 1 2 2 2 1 1
## 0.666 0.6672 0.6682 0.6688 0.67 0.6742 0.676 0.6772 0.6775 0.6777
## 3 1 1 1 2 1 1 1 1 1
## 0.678 0.68 0.6801 0.6811 0.6825 0.6828 0.6847 0.685 0.6854 0.6862
## 2 3 1 1 1 1 1 1 1 1
## 0.6871 0.6889 0.6899 0.6901 0.6907 0.6908 0.6914 0.6916 0.6917 0.6926
## 2 1 2 1 1 1 1 1 1 1
## 0.6928 0.6929 0.6941 0.6949 0.6951 0.6952 0.6967 0.6978 0.7009 0.7019
## 1 1 1 2 1 2 1 1 1 1
## 0.7023 0.7033 0.7039 0.7049 0.7059 0.7066 0.7079 0.7082 0.7099 0.7115
## 1 1 1 3 1 1 1 1 1 1
## 0.7119 0.7123 0.7133 0.7139 0.7147 0.7154 0.7155 0.7159 0.7163 0.7166
## 1 1 1 1 1 2 1 2 1 1
## 0.7206 0.7219 0.7239 0.7266 0.7269 0.7271 0.7279 0.7286 0.7299 0.7319
## 1 1 1 1 2 1 1 1 1 2
## 0.7331 0.7354 0.7359 0.7366 0.7372 0.7377 0.7379 0.7391 0.7401 0.7409
## 1 1 3 1 1 1 1 1 1 1
## 0.7414 0.7433 0.7434 0.7439 0.7448 0.745 0.7453 0.7479 0.7512 0.7528
## 1 1 1 1 1 1 1 1 1 1
## 0.755 0.756 0.7561 0.757 0.7571 0.76 0.7607 0.7621 0.763 0.764
## 1 1 2 1 1 1 1 1 1 1
## 0.7647 0.7651 0.7666 0.7672 0.768 0.7684 0.7689 0.7695 0.77 0.7723
## 2 1 1 1 1 1 1 1 2 1
## 0.7726 0.7731 0.7732 0.7733 0.774 0.7749 0.7754 0.7768 0.777 0.7782
## 1 1 1 2 2 1 1 1 1 1
## 0.7783 0.7796 0.78 0.7816 0.7826 0.7828 0.7846 0.7849 0.7857 0.7866
## 1 1 4 1 1 1 1 1 2 3
## 0.7872 0.7874 0.7876 0.788 0.7885 0.7894 0.7923 0.7933 0.7937 0.794
## 1 1 1 1 1 1 1 1 1 1
## 0.7943 0.7956 0.7965 0.8 0.8003 0.8007 0.8013 0.802 0.8028 0.8035
## 1 1 1 3 1 2 2 1 1 1
## 0.8044 0.8056 0.8066 0.8086 0.8095 0.81 0.8103 0.8114 0.8159 0.8169
## 1 1 1 1 1 1 1 1 2 1
## 0.8174 0.8199 0.8208 0.8209 0.8221 0.8239 0.8249 0.8257 0.8259 0.8266
## 1 1 1 1 1 1 2 1 1 1
## 0.8283 0.8289 0.8294 0.8299 0.8305 0.8306 0.8319 0.8323 0.8324 0.8333
## 1 1 1 1 1 1 1 1 1 1
## 0.8341 0.8348 0.8349 0.8355 0.8356 0.8367 0.8368 0.8369 0.8371 0.8379
## 1 1 2 1 1 1 2 1 1 3
## 0.8386 0.8389 0.839 0.8394 0.8399 0.8408 0.841 0.8429 0.8431 0.8433
## 2 3 1 1 2 1 1 1 1 2
## 0.8439 0.8446 0.8448 0.8457 0.8458 0.8463 0.8466 0.8475 0.8479 0.848
## 2 1 1 1 1 1 3 1 2 1
## 0.8485 0.8499 0.8507 0.8509 0.8519 0.8529 0.853 0.8538 0.8552 0.8553
## 1 2 1 1 1 1 2 1 1 2
## 0.8555 0.8559 0.8561 0.8566 0.8569 0.8579 0.8592 0.8599 0.8619 0.8623
## 1 2 1 1 1 1 1 2 3 2
## 0.8625 0.8629 0.8633 0.8642 0.8651 0.8652 0.8659 0.8666 0.8675 0.8683
## 1 1 2 1 1 1 1 2 1 1
## 0.8695 0.87 0.871 0.8712 0.8716 0.872 0.8728 0.8739 0.875 0.8752
## 1 4 1 1 1 1 1 1 1 1
## 0.8758 0.876 0.8763 0.8771 0.878 0.8785 0.8789 0.879 0.8792 0.8797
## 1 2 1 1 1 1 1 1 1 1
## 0.88 0.8828 0.8833 0.884 0.8855 0.886 0.8863 0.8866 0.8869 0.8874
## 5 1 2 2 1 1 1 2 1 1
## 0.8893 0.89 0.8905 0.8907 0.8912 0.892 0.8922 0.8924 0.8925 0.8933
## 1 1 1 1 1 1 1 1 1 3
## 0.8936 0.8938 0.8946 0.895 0.8966 0.8974 0.898 0.8984 0.8987 0.9
## 1 1 1 1 3 2 1 1 1 3
## 0.9009 0.9011 0.9013 0.9019 0.902 0.9024 0.9042 0.9045 0.9049 0.905
## 1 1 1 1 2 1 1 1 1 3
## 0.9052 0.9053 0.906 0.9062 0.9063 0.9066 0.9068 0.9069 0.907 0.9076
## 1 1 1 2 1 3 1 1 1 1
## 0.908 0.9085 0.9087 0.9088 0.909 0.9104 0.9108 0.9114 0.9116 0.9118
## 1 1 1 1 2 1 1 2 1 1
## 0.913 0.9133 0.9152 0.9157 0.916 0.9162 0.9166 0.9171 0.9176 0.9177
## 1 1 1 1 1 1 4 1 1 1
## 0.9185 0.9186 0.9193 0.92 0.9212 0.9216 0.9224 0.923 0.9233 0.9234
## 1 1 1 3 1 1 1 1 1 1
## 0.9238 0.924 0.925 0.9254 0.9255 0.9258 0.9266 0.927 0.9274 0.9277
## 1 3 1 1 1 1 2 1 2 1
## 0.9279 0.928 0.9287 0.9291 0.9295 0.93 0.9307 0.9312 0.9318 0.9332
## 1 1 1 1 1 9 2 1 1 1
## 0.9333 0.934 0.9346 0.935 0.9352 0.9353 0.9355 0.936 0.9363 0.9366
## 1 2 1 3 1 2 1 1 1 4
## 0.9368 0.9371 0.9375 0.9378 0.9379 0.9393 0.9394 0.9399 0.9401 0.9419
## 1 2 1 1 3 1 1 3 1 1
## 0.9425 0.9429 0.9432 0.9439 0.9442 0.9446 0.9449 0.9454 0.9457 0.9458
## 1 1 1 1 2 1 2 1 1 1
## 0.9459 0.9464 0.9472 0.9479 0.9485 0.9488 0.9495 0.9499 0.9507 0.9508
## 2 1 1 3 1 1 1 6 1 1
## 0.9509 0.9511 0.9512 0.9513 0.9515 0.9516 0.9519 0.9522 0.9525 0.9529
## 3 1 1 2 1 1 2 1 1 1
## 0.9533 0.9538 0.9541 0.9542 0.9552 0.9555 0.9556 0.9557 0.9559 0.9562
## 1 1 1 2 2 1 1 1 2 3
## 0.9563 0.9568 0.9573 0.9575 0.9576 0.9578 0.9579 0.958 0.9583 0.9589
## 1 1 2 1 2 2 2 1 1 1
## 0.9594 0.9599 0.9609 0.9612 0.9619 0.9621 0.9625 0.9628 0.9633 0.9636
## 1 3 1 1 1 2 1 3 2 1
## 0.9639 0.9642 0.9644 0.9645 0.9646 0.9649 0.9653 0.9656 0.9657 0.9658
## 2 1 1 1 2 1 2 1 1 1
## 0.9659 0.9661 0.9663 0.9666 0.9672 0.9673 0.9675 0.9679 0.9682 0.9684
## 2 1 2 3 1 1 1 1 1 1
## 0.9686 0.9696 0.9697 0.9699 0.97 0.9706 0.9707 0.9708 0.9715 0.9716
## 1 1 1 2 1 1 1 2 1 1
## 0.9719 0.9724 0.9725 0.9727 0.9738 0.9739 0.9741 0.9742 0.9748 0.9749
## 1 1 1 1 1 2 1 1 1 5
## 0.9753 0.9754 0.9756 0.9757 0.9758 0.9761 0.9762 0.9763 0.9764 0.9766
## 1 1 1 1 1 1 1 1 1 1
## 0.9771 0.9775 0.9776 0.9777 0.9778 0.9783 0.9785 0.9799 0.9802 0.9806
## 1 2 1 3 1 1 1 9 2 2
## 0.9808 0.9814 0.9816 0.9819 0.9821 0.9822 0.9825 0.9828 0.983 0.9831
## 1 1 1 1 2 1 2 1 1 1
## 0.9833 0.9835 0.9848 0.9849 0.9853 0.9854 0.9856 0.9859 0.9861 0.9866
## 3 1 1 2 1 1 1 1 1 3
## 0.9869 0.987 0.9872 0.9875 0.9879 0.9881 0.9883 0.9887 0.9893 0.9894
## 1 2 1 3 2 2 1 1 1 1
## 0.9897 0.9899 0.9901 0.9902 0.9903 0.9905 0.9906 0.9911 0.9914 0.9918
## 1 3 1 1 1 1 3 1 2 1
## 0.9919 0.9925 0.9928 0.993 0.9931 0.9933 0.9935 0.9939 0.9945 0.9946
## 3 1 2 1 1 1 1 2 3 1
## 0.9947 0.995 0.9951 0.9953 0.9959 0.996 0.9963 0.9965 0.9966 0.9967
## 1 4 1 3 1 7 1 1 2 1
## 0.9968 0.997 0.9979 0.998 0.9982 0.9983 0.9986 0.9987 0.9988 0.999
## 1 1 1 2 1 1 1 1 1 1
## 0.9991 0.9992 0.9993 1 1.0005 1.0006 1.0011 1.0013 1.0014 1.0015
## 1 1 1 52 1 1 1 2 1 1
## 1.002 1.0022 1.0024 1.0025 1.0027 1.0029 1.0033 1.0035 1.0037 1.0039
## 3 2 2 1 1 1 1 2 1 1
## 1.0042 1.0044 1.0045 1.0049 1.005 1.0051 1.0053 1.0054 1.0055 1.0057
## 1 1 1 2 1 1 1 1 1 1
## 1.0058 1.006 1.0061 1.0062 1.0066 1.0076 1.0079 1.008 1.0082 1.0084
## 1 2 1 2 4 1 1 2 1 2
## 1.0086 1.009 1.0091 1.0093 1.0094 1.0102 1.0105 1.0109 1.0111 1.0112
## 1 1 3 1 1 1 2 1 1 1
## 1.0114 1.0118 1.012 1.0121 1.0122 1.0123 1.0124 1.0125 1.0126 1.0127
## 3 1 3 1 1 1 1 1 3 1
## 1.0128 1.0129 1.0131 1.0133 1.0135 1.0138 1.014 1.0141 1.0142 1.0144
## 2 1 2 2 1 2 1 1 2 2
## 1.0145 1.0146 1.0148 1.0149 1.0152 1.0154 1.0156 1.0158 1.0159 1.016
## 1 1 2 2 1 2 3 1 1 4
## 1.0162 1.0166 1.0169 1.017 1.0172 1.0173 1.0174 1.0175 1.0176 1.0178
## 1 2 2 2 2 1 1 1 1 1
## 1.018 1.0184 1.0185 1.0187 1.0189 1.0192 1.0195 1.0196 1.02 1.0204
## 1 1 1 1 1 1 1 1 9 2
## 1.0207 1.0209 1.0213 1.0214 1.0216 1.022 1.0223 1.0224 1.0225 1.0226
## 1 1 1 1 1 3 1 1 2 1
## 1.0227 1.023 1.0232 1.0233 1.0234 1.0236 1.0238 1.0239 1.024 1.0241
## 2 2 2 2 2 1 1 1 4 1
## 1.0243 1.0244 1.0247 1.0249 1.0253 1.0254 1.0255 1.0256 1.0257 1.0261
## 2 1 1 5 4 1 1 1 1 2
## 1.0265 1.0266 1.0272 1.0274 1.0275 1.0278 1.028 1.0284 1.0285 1.0286
## 1 2 1 1 1 1 4 1 2 3
## 1.0287 1.0289 1.0291 1.0293 1.0294 1.03 1.0302 1.0303 1.0306 1.0307
## 5 1 1 2 1 7 1 1 1 1
## 1.0308 1.0309 1.031 1.0311 1.0313 1.0316 1.0317 1.0319 1.032 1.0321
## 1 2 2 2 1 2 2 1 1 1
## 1.0322 1.0325 1.0326 1.0328 1.0329 1.033 1.0331 1.0333 1.0335 1.0337
## 1 1 1 1 1 1 1 1 1 1
## 1.034 1.0341 1.0345 1.0346 1.0349 1.0353 1.0357 1.036 1.0362 1.0364
## 1 1 1 2 1 1 1 1 1 2
## 1.0365 1.0367 1.0369 1.0374 1.0375 1.038 1.0381 1.0386 1.0388 1.0389
## 1 2 2 2 1 4 1 3 2 3
## 1.0391 1.0394 1.04 1.0402 1.0404 1.0405 1.041 1.0411 1.0413 1.0415
## 1 1 8 1 1 2 2 2 1 2
## 1.042 1.0422 1.0423 1.0425 1.0426 1.0427 1.0428 1.0429 1.0431 1.0432
## 2 1 1 2 1 1 1 1 1 1
## 1.0433 1.0434 1.0437 1.044 1.0441 1.0442 1.0445 1.0446 1.0449 1.0452
## 2 1 1 5 1 1 2 1 8 1
## 1.0453 1.0454 1.0456 1.0457 1.046 1.0461 1.0463 1.0464 1.0465 1.0466
## 1 1 2 1 1 1 1 1 1 4
## 1.0469 1.047 1.0471 1.0472 1.0473 1.0477 1.048 1.0482 1.0485 1.0488
## 1 1 1 1 1 1 3 1 1 1
## 1.0489 1.0491 1.0493 1.0494 1.0498 1.05 1.0501 1.0507 1.0509 1.0513
## 1 1 2 1 1 5 1 2 2 1
## 1.0515 1.0516 1.0518 1.052 1.0521 1.0522 1.0525 1.0526 1.0528 1.0529
## 1 2 1 3 1 1 1 1 2 2
## 1.0531 1.0532 1.0533 1.0534 1.0535 1.0537 1.0538 1.0539 1.054 1.0542
## 2 3 2 3 1 2 1 1 2 1
## 1.0543 1.0544 1.0545 1.0546 1.0547 1.0548 1.0549 1.0552 1.0553 1.0556
## 2 1 3 3 1 2 6 1 1 1
## 1.0557 1.0558 1.0559 1.056 1.0561 1.0564 1.0565 1.0566 1.0569 1.0571
## 2 1 1 3 1 1 1 4 2 1
## 1.0573 1.0576 1.0578 1.058 1.0581 1.0583 1.0586 1.0589 1.059 1.0591
## 1 1 2 1 1 1 1 3 1 3
## 1.0595 1.0596 1.06 1.0604 1.0606 1.0608 1.0609 1.0611 1.0612 1.0615
## 2 3 2 1 1 3 2 2 4 2
## 1.0616 1.0617 1.062 1.0625 1.0626 1.0628 1.0629 1.063 1.0633 1.0634
## 3 1 4 1 3 1 4 1 4 3
## 1.0636 1.0638 1.064 1.0642 1.0643 1.0646 1.0648 1.0649 1.0652 1.0653
## 2 1 4 1 1 3 1 3 1 1
## 1.0654 1.0655 1.0656 1.0657 1.0658 1.0659 1.066 1.0661 1.0664 1.0665
## 1 1 1 1 1 3 3 1 2 3
## 1.0666 1.0668 1.0669 1.0674 1.0675 1.0676 1.0678 1.0679 1.068 1.0682
## 2 1 2 4 1 1 1 1 8 1
## 1.0683 1.0687 1.069 1.0691 1.0692 1.0695 1.0696 1.0698 1.07 1.0705
## 1 1 1 2 1 2 1 1 2 1
## 1.0709 1.071 1.0711 1.0712 1.0714 1.0715 1.0717 1.072 1.0722 1.0723
## 2 2 2 1 3 3 1 2 1 1
## 1.0725 1.073 1.0731 1.0732 1.0736 1.0738 1.074 1.0742 1.0744 1.0745
## 1 3 1 1 2 1 2 3 2 1
## 1.0746 1.0747 1.0749 1.075 1.0753 1.0755 1.0757 1.0758 1.076 1.0762
## 2 2 1 4 4 1 1 1 1 1
## 1.0764 1.0766 1.0768 1.0771 1.0773 1.0774 1.0775 1.0777 1.0779 1.078
## 1 3 1 1 2 2 2 2 1 2
## 1.0782 1.0784 1.0785 1.0787 1.0788 1.079 1.0792 1.0793 1.0794 1.0798
## 1 1 1 1 1 2 1 1 1 1
## 1.08 1.0801 1.0803 1.0812 1.0813 1.0815 1.0816 1.0817 1.082 1.0821
## 10 1 1 1 1 1 2 2 3 1
## 1.0822 1.0825 1.0826 1.0828 1.0832 1.0833 1.0834 1.084 1.0842 1.0843
## 1 3 1 1 2 4 3 4 1 1
## 1.0846 1.0847 1.0848 1.085 1.0853 1.0855 1.0856 1.0857 1.0858 1.086
## 1 1 3 6 1 1 1 1 1 5
## 1.0861 1.0862 1.0866 1.0868 1.0869 1.087 1.0871 1.0872 1.0873 1.0874
## 1 1 1 1 2 1 2 1 1 3
## 1.0875 1.0878 1.088 1.0882 1.0885 1.089 1.0891 1.0897 1.09 1.0901
## 2 1 5 1 1 1 1 1 11 1
## 1.0904 1.0905 1.0906 1.091 1.0911 1.0912 1.0916 1.0918 1.0922 1.0924
## 1 6 1 1 2 1 1 1 1 1
## 1.0925 1.0927 1.0928 1.093 1.0932 1.0933 1.0934 1.0936 1.0937 1.094
## 3 1 3 1 1 3 1 1 1 4
## 1.0941 1.0943 1.0946 1.0948 1.0949 1.0952 1.0953 1.0954 1.0955 1.0957
## 1 2 1 2 2 1 1 1 1 2
## 1.096 1.0961 1.0962 1.0963 1.0964 1.0966 1.0967 1.0968 1.0969 1.097
## 4 1 1 1 1 5 1 1 1 4
## 1.0972 1.0973 1.0974 1.0975 1.0976 1.0977 1.0978 1.098 1.0982 1.0983
## 1 1 5 2 1 1 1 2 1 1
## 1.0986 1.0987 1.099 1.0991 1.0994 1.0996 1.1 1.1001 1.1002 1.1005
## 1 1 1 1 1 1 10 1 1 1
## 1.1007 1.101 1.1014 1.1015 1.1018 1.102 1.1021 1.1023 1.1026 1.1027
## 2 1 2 1 1 3 1 1 2 2
## 1.103 1.1032 1.1041 1.1042 1.105 1.1052 1.1053 1.1056 1.1058 1.106
## 1 1 1 2 6 1 2 1 1 1
## 1.1061 1.1062 1.1063 1.1065 1.1066 1.107 1.1073 1.1074 1.108 1.1082
## 3 1 2 1 1 2 1 2 1 2
## 1.1083 1.1084 1.1085 1.1087 1.109 1.1092 1.1095 1.11 1.1104 1.1105
## 3 1 2 1 2 1 1 13 2 2
## 1.1108 1.111 1.1111 1.1114 1.1116 1.1117 1.1118 1.112 1.1122 1.1123
## 1 4 1 1 2 1 2 2 3 1
## 1.1124 1.1125 1.1126 1.1127 1.1128 1.1129 1.113 1.1133 1.1135 1.1137
## 2 1 1 1 2 1 2 6 2 2
## 1.1139 1.114 1.1147 1.1149 1.115 1.1151 1.1153 1.1154 1.1157 1.1159
## 1 3 2 2 4 1 1 6 1 1
## 1.116 1.1161 1.1163 1.1164 1.1166 1.1169 1.1171 1.1173 1.1177 1.1178
## 1 1 1 3 3 1 1 2 1 1
## 1.1179 1.118 1.1183 1.1184 1.1187 1.1188 1.119 1.1191 1.1192 1.1196
## 1 2 1 1 1 1 2 1 2 1
## 1.12 1.1201 1.1204 1.1206 1.1207 1.121 1.1216 1.1217 1.1218 1.122
## 9 2 3 3 1 2 1 1 2 2
## 1.1223 1.1225 1.1228 1.123 1.1231 1.1233 1.1234 1.1235 1.1237 1.1238
## 2 1 2 2 1 6 1 2 2 1
## 1.1239 1.124 1.1242 1.1243 1.1245 1.1246 1.1247 1.125 1.1252 1.1254
## 1 4 1 1 1 1 1 1 2 1
## 1.1255 1.1257 1.1259 1.1266 1.1269 1.1272 1.1274 1.1276 1.1279 1.1282
## 1 1 2 6 2 2 2 2 5 1
## 1.1283 1.1284 1.1285 1.1287 1.1289 1.129 1.1295 1.1299 1.1301 1.1303
## 2 1 1 1 1 3 1 12 1 1
## 1.1306 1.1307 1.1308 1.1311 1.1312 1.1314 1.1315 1.1316 1.1319 1.1321
## 1 1 1 1 1 3 1 1 5 1
## 1.1325 1.1327 1.1328 1.1329 1.133 1.1333 1.1335 1.1337 1.1339 1.1341
## 2 1 2 1 1 4 1 1 6 1
## 1.1342 1.1343 1.1345 1.135 1.1351 1.1354 1.1355 1.1356 1.1359 1.1363
## 1 2 1 3 1 1 2 1 7 1
## 1.1366 1.1367 1.1368 1.1369 1.1372 1.1373 1.1375 1.1377 1.1379 1.138
## 2 1 1 1 2 3 2 3 2 1
## 1.1381 1.1382 1.1383 1.1385 1.1386 1.1388 1.1389 1.139 1.1396 1.1399
## 1 1 1 3 1 1 2 1 1 8
## 1.1403 1.1407 1.1408 1.141 1.1411 1.1414 1.1415 1.1419 1.1421 1.1428
## 1 1 2 1 1 1 2 3 1 1
## 1.1431 1.1433 1.1434 1.1439 1.1441 1.1443 1.1446 1.145 1.1451 1.1453
## 1 6 3 6 1 2 1 2 2 1
## 1.1454 1.1455 1.1456 1.1459 1.1461 1.1462 1.1463 1.1465 1.1466 1.1468
## 1 1 1 2 1 1 1 4 3 1
## 1.147 1.1471 1.1475 1.1476 1.1477 1.1479 1.1481 1.1482 1.1483 1.1486
## 1 1 2 2 2 4 1 1 1 2
## 1.1488 1.1489 1.149 1.1492 1.1494 1.1495 1.1497 1.1499 1.1502 1.1508
## 3 1 2 1 2 1 1 17 1 2
## 1.1509 1.1511 1.1514 1.1515 1.1516 1.1519 1.1521 1.1525 1.1526 1.1527
## 1 2 2 1 2 6 2 1 1 1
## 1.1529 1.1533 1.1537 1.1539 1.1542 1.1543 1.1544 1.1546 1.155 1.1551
## 1 2 1 1 1 1 1 1 5 1
## 1.1552 1.1556 1.1557 1.1559 1.1563 1.1566 1.157 1.1571 1.1573 1.1575
## 1 2 1 4 1 2 2 1 1 3
## 1.1579 1.1581 1.1582 1.1583 1.1585 1.1588 1.159 1.1591 1.1592 1.1593
## 4 1 1 3 2 1 2 1 3 1
## 1.1595 1.1599 1.1601 1.1605 1.1606 1.1607 1.1608 1.161 1.1613 1.1615
## 1 9 1 1 1 3 2 1 2 1
## 1.1617 1.1619 1.1623 1.1625 1.163 1.1633 1.1637 1.1638 1.1639 1.165
## 1 4 2 3 1 5 1 1 8 4
## 1.1653 1.1655 1.1656 1.1659 1.1662 1.1666 1.1667 1.167 1.1671 1.1672
## 2 3 1 4 1 2 1 1 1 1
## 1.1675 1.1676 1.1677 1.1679 1.1683 1.1684 1.1685 1.1689 1.1692 1.1695
## 1 3 1 1 1 1 1 1 2 2
## 1.1697 1.1699 1.1702 1.1704 1.1705 1.1707 1.1708 1.171 1.1711 1.1712
## 1 7 1 1 1 1 1 3 2 1
## 1.1713 1.1716 1.1719 1.1721 1.1725 1.1727 1.1729 1.1733 1.1738 1.1739
## 1 2 4 1 1 3 1 8 2 6
## 1.174 1.1746 1.175 1.1755 1.1757 1.1759 1.1762 1.1763 1.1764 1.1765
## 1 1 5 2 1 6 2 3 1 1
## 1.1766 1.1769 1.177 1.1771 1.1772 1.1773 1.1775 1.1779 1.178 1.1781
## 8 2 1 1 1 1 2 5 1 1
## 1.1783 1.1786 1.1787 1.1791 1.1793 1.1795 1.1796 1.1799 1.1802 1.1804
## 2 2 1 2 1 2 1 7 1 1
## 1.181 1.1815 1.1816 1.1819 1.1821 1.1825 1.1826 1.1828 1.183 1.1833
## 1 1 2 4 2 4 2 1 1 8
## 1.1835 1.1836 1.1837 1.1839 1.184 1.1845 1.1846 1.185 1.1853 1.1854
## 1 1 1 9 1 1 1 3 1 1
## 1.1855 1.1856 1.1857 1.1858 1.1859 1.1861 1.1862 1.1863 1.187 1.1875
## 2 1 2 1 5 1 1 1 1 5
## 1.1877 1.1879 1.1881 1.1882 1.1884 1.1885 1.1887 1.189 1.1892 1.1893
## 3 5 2 2 1 2 2 1 1 1
## 1.1897 1.1899 1.1904 1.1907 1.1909 1.1914 1.1915 1.1916 1.1919 1.1921
## 1 17 2 2 1 1 1 1 5 1
## 1.1923 1.1924 1.1926 1.193 1.1933 1.1935 1.1937 1.194 1.1941 1.1942
## 1 4 2 3 8 1 2 1 1 2
## 1.1944 1.1947 1.1948 1.195 1.1951 1.1952 1.1957 1.1959 1.196 1.1961
## 2 1 1 1 1 1 3 1 7 3
## 1.1962 1.1964 1.1966 1.1971 1.1973 1.1975 1.1977 1.1978 1.198 1.1988
## 1 1 11 2 1 2 1 1 7 1
## 1.1989 1.199 1.1992 1.2 1.2004 1.201 1.2011 1.2013 1.2018 1.202
## 1 2 1 13 1 1 1 2 2 4
## 1.2023 1.2024 1.2026 1.2027 1.2028 1.2031 1.2033 1.2035 1.2036 1.2038
## 1 3 1 3 2 1 7 1 1 1
## 1.204 1.2042 1.2044 1.2048 1.205 1.2053 1.2054 1.2057 1.2058 1.206
## 4 1 1 1 8 3 2 2 1 4
## 1.2066 1.2067 1.2068 1.2075 1.2076 1.2077 1.2078 1.208 1.2083 1.2085
## 4 1 1 2 1 1 1 3 1 2
## 1.2088 1.2089 1.209 1.2091 1.2092 1.2095 1.21 1.2103 1.2104 1.2105
## 1 1 1 1 1 1 13 1 1 1
## 1.2106 1.2108 1.211 1.2111 1.2113 1.2114 1.2118 1.212 1.2122 1.2125
## 1 1 2 3 1 2 1 2 1 1
## 1.213 1.2133 1.2137 1.2139 1.2141 1.2147 1.215 1.2152 1.2157 1.2159
## 1 9 1 1 2 1 6 1 2 1
## 1.216 1.2163 1.2165 1.2166 1.2167 1.2168 1.217 1.2171 1.2172 1.2173
## 5 1 1 5 1 1 1 3 1 2
## 1.2174 1.2175 1.2176 1.2177 1.218 1.2181 1.2183 1.2184 1.2185 1.2188
## 1 10 4 1 4 2 2 2 1 1
## 1.2193 1.2194 1.2195 1.22 1.2208 1.2212 1.2213 1.2214 1.2215 1.2216
## 1 1 2 19 1 1 2 4 1 1
## 1.222 1.2224 1.2228 1.2231 1.2233 1.2234 1.224 1.2242 1.2244 1.2246
## 2 6 2 1 10 1 3 1 1 2
## 1.2247 1.225 1.2254 1.2255 1.2256 1.2257 1.2258 1.226 1.2261 1.2262
## 1 4 1 1 1 1 1 1 1 1
## 1.2263 1.2265 1.2266 1.2268 1.2271 1.2273 1.2275 1.2276 1.2277 1.228
## 2 1 14 1 3 1 5 2 1 4
## 1.2283 1.2285 1.2286 1.2287 1.2289 1.2298 1.23 1.2302 1.2303 1.2305
## 2 1 1 2 1 1 17 2 1 2
## 1.2309 1.2312 1.2314 1.2315 1.232 1.2324 1.2328 1.2329 1.233 1.2333
## 1 1 4 2 3 3 1 1 3 12
## 1.2336 1.2337 1.2338 1.2339 1.234 1.2342 1.2343 1.2344 1.2345 1.2346
## 1 1 1 1 1 2 2 1 1 4
## 1.2347 1.2348 1.2349 1.235 1.2352 1.2354 1.2358 1.2359 1.236 1.2361
## 1 1 1 6 1 1 1 1 9 4
## 1.2363 1.2364 1.2366 1.237 1.2373 1.2375 1.238 1.2381 1.2382 1.2384
## 1 2 13 1 2 1 1 2 1 1
## 1.2385 1.2386 1.2395 1.2396 1.2397 1.24 1.2406 1.2407 1.2411 1.2412
## 1 2 1 1 1 29 1 1 1 2
## 1.2414 1.2415 1.2416 1.2418 1.242 1.2423 1.2424 1.2427 1.2428 1.2433
## 2 1 1 1 2 1 1 1 2 4
## 1.244 1.2442 1.2445 1.2448 1.245 1.2451 1.2452 1.2453 1.2454 1.2457
## 4 1 1 1 11 1 1 1 1 1
## 1.2458 1.2462 1.2466 1.2467 1.2469 1.247 1.2471 1.2472 1.2475 1.2477
## 3 1 9 1 1 2 1 1 5 1
## 1.248 1.2481 1.2482 1.2483 1.2485 1.2487 1.2488 1.249 1.2491 1.2492
## 5 1 3 1 1 2 1 1 1 1
## 1.2494 1.2495 1.2496 1.2497 1.25 1.2504 1.2506 1.2507 1.2509 1.2514
## 1 1 1 1 17 1 1 2 1 1
## 1.2515 1.2516 1.252 1.2524 1.2529 1.2533 1.2537 1.254 1.2546 1.2549
## 1 3 8 6 1 14 2 3 3 3
## 1.256 1.2561 1.2562 1.2566 1.2568 1.2571 1.2572 1.2575 1.2576 1.2577
## 1 1 1 26 1 1 1 3 2 2
## 1.258 1.2583 1.2586 1.2587 1.2588 1.2589 1.26 1.2607 1.2609 1.2612
## 1 4 1 1 1 1 26 1 1 2
## 1.2613 1.2614 1.262 1.2623 1.2625 1.2626 1.2628 1.2629 1.2633 1.2634
## 2 1 3 1 2 1 1 2 12 2
## 1.2635 1.2638 1.264 1.2642 1.2648 1.2649 1.2653 1.2655 1.2657 1.266
## 1 1 2 1 1 2 1 3 2 2
## 1.2662 1.2666 1.2669 1.2671 1.2672 1.2675 1.268 1.2681 1.2683 1.2686
## 3 17 1 1 1 1 7 1 2 1
## 1.2687 1.2688 1.269 1.2692 1.2695 1.2696 1.27 1.2704 1.2705 1.2712
## 1 1 1 1 1 2 19 1 1 1
## 1.2714 1.2715 1.2716 1.272 1.2722 1.2725 1.2726 1.2727 1.2728 1.2733
## 1 1 1 7 1 6 2 1 1 10
## 1.2734 1.2737 1.274 1.2745 1.2746 1.2749 1.2751 1.2756 1.2757 1.276
## 1 1 2 1 1 9 1 1 3 3
## 1.2762 1.2764 1.2766 1.277 1.2775 1.278 1.2781 1.2783 1.2784 1.2785
## 1 1 11 1 2 1 1 1 1 2
## 1.2789 1.28 1.2806 1.2809 1.2811 1.2813 1.2814 1.282 1.2821 1.2822
## 1 20 1 1 1 1 1 1 1 1
## 1.2825 1.2828 1.283 1.2832 1.2833 1.2836 1.2837 1.284 1.2844 1.2846
## 1 1 1 1 9 1 1 8 1 1
## 1.2847 1.2849 1.2853 1.2854 1.2857 1.286 1.2862 1.2866 1.2869 1.2871
## 2 6 3 2 2 3 1 12 1 3
## 1.2875 1.2876 1.2877 1.288 1.2882 1.2888 1.2889 1.2892 1.2893 1.2894
## 2 1 1 4 1 1 1 2 1 1
## 1.29 1.2904 1.2905 1.2914 1.2915 1.292 1.2923 1.2925 1.2933 1.2936
## 17 2 1 1 1 4 1 7 16 1
## 1.294 1.2941 1.2945 1.2946 1.2947 1.2949 1.2955 1.296 1.2961 1.2963
## 3 1 1 2 1 3 1 4 1 1
## 1.2964 1.2965 1.2966 1.2968 1.2971 1.2972 1.2973 1.2975 1.2977 1.298
## 1 1 11 1 1 1 2 1 1 3
## 1.2981 1.2982 1.2983 1.2986 1.2988 1.2992 1.2993 1.3 1.3009 1.3014
## 1 1 1 1 1 2 1 19 1 1
## 1.3016 1.302 1.3024 1.3025 1.3028 1.3033 1.3037 1.304 1.3049 1.3057
## 1 3 1 3 1 12 1 1 4 1
## 1.306 1.3061 1.3066 1.3071 1.3075 1.3077 1.3081 1.3083 1.3085 1.3087
## 6 1 15 1 3 1 1 6 1 3
## 1.3088 1.3092 1.3094 1.3097 1.31 1.3111 1.3112 1.3116 1.3118 1.312
## 1 2 1 1 28 1 1 2 1 2
## 1.3122 1.3123 1.3125 1.3127 1.3128 1.3131 1.3133 1.3134 1.3137 1.3138
## 1 1 2 1 1 1 24 1 1 1
## 1.314 1.3142 1.3149 1.3151 1.3152 1.316 1.3165 1.3166 1.3172 1.3174
## 1 1 6 1 1 4 1 12 2 1
## 1.318 1.3183 1.3187 1.3188 1.3191 1.32 1.3207 1.3212 1.322 1.3224
## 3 3 2 2 1 19 1 1 3 1
## 1.3225 1.3228 1.3233 1.3237 1.3239 1.324 1.3244 1.3247 1.325 1.3254
## 5 2 10 1 1 3 1 1 4 2
## 1.3257 1.326 1.3262 1.3264 1.3265 1.3266 1.327 1.3274 1.3276 1.3277
## 1 4 1 1 1 8 1 1 1 2
## 1.328 1.3283 1.3287 1.329 1.3294 1.33 1.3303 1.3306 1.3307 1.3308
## 3 1 1 2 1 11 1 1 2 1
## 1.3314 1.3316 1.3317 1.332 1.3322 1.3325 1.333 1.3333 1.334 1.3342
## 3 1 1 3 1 1 1 8 1 4
## 1.3343 1.335 1.3351 1.3354 1.3357 1.336 1.3362 1.3366 1.3371 1.3374
## 1 5 1 1 1 2 2 5 1 5
## 1.338 1.3383 1.3387 1.34 1.3407 1.3409 1.341 1.3412 1.3413 1.3416
## 5 4 1 25 1 1 1 2 1 1
## 1.342 1.3425 1.3429 1.343 1.3432 1.3433 1.3436 1.344 1.3442 1.3443
## 2 3 1 1 1 7 1 7 1 1
## 1.3444 1.3446 1.3448 1.345 1.3466 1.3471 1.3472 1.3474 1.3478 1.348
## 2 1 1 9 5 1 1 2 1 5
## 1.3483 1.3485 1.3487 1.3496 1.35 1.3502 1.3508 1.3512 1.3517 1.352
## 2 1 2 1 12 1 1 2 1 3
## 1.3521 1.3525 1.3531 1.3533 1.354 1.3544 1.355 1.356 1.3566 1.3571
## 1 1 1 10 3 1 6 6 11 2
## 1.3574 1.3581 1.3583 1.3587 1.36 1.3612 1.3614 1.3617 1.3618 1.362
## 2 1 2 1 10 1 1 1 2 1
## 1.3625 1.3633 1.3637 1.364 1.3643 1.3644 1.365 1.3657 1.366 1.3666
## 2 10 1 3 1 1 3 1 1 8
## 1.3672 1.3674 1.368 1.3688 1.3689 1.369 1.3691 1.37 1.3711 1.3714
## 2 1 1 1 1 1 1 20 1 2
## 1.3716 1.3718 1.372 1.3723 1.3725 1.3733 1.3735 1.3737 1.374 1.3742
## 1 1 3 1 2 10 1 1 2 1
## 1.375 1.3757 1.3758 1.3759 1.3762 1.3763 1.3764 1.3766 1.3774 1.3775
## 5 1 1 3 3 2 1 10 3 1
## 1.3779 1.3783 1.3799 1.3814 1.3816 1.3819 1.3821 1.3825 1.3833 1.3836
## 1 1 17 1 3 2 1 3 15 1
## 1.3837 1.3839 1.3844 1.385 1.3854 1.3857 1.3859 1.3862 1.3866 1.3871
## 1 1 1 5 1 2 2 1 9 1
## 1.3875 1.3879 1.3885 1.3888 1.389 1.3899 1.3912 1.3914 1.3919 1.3925
## 2 4 1 1 2 14 1 1 1 2
## 1.3933 1.3937 1.3938 1.3939 1.3942 1.3946 1.395 1.3959 1.3961 1.3962
## 15 2 1 2 3 1 1 2 1 1
## 1.3963 1.3966 1.3969 1.3975 1.3979 1.3983 1.3985 1.3986 1.399 1.3999
## 1 17 1 1 1 2 1 1 1 21
## 1.4009 1.4014 1.4016 1.4018 1.4019 1.4023 1.4033 1.4037 1.4039 1.405
## 1 1 1 1 1 2 12 1 1 1
## 1.4058 1.4059 1.4066 1.4079 1.4085 1.4095 1.4097 1.4099 1.4106 1.4112
## 1 2 17 1 1 1 1 16 1 3
## 1.4119 1.4125 1.4133 1.4138 1.4139 1.415 1.4154 1.4155 1.4159 1.4162
## 2 2 14 1 1 6 1 1 2 2
## 1.4166 1.4169 1.4175 1.4179 1.4183 1.4185 1.419 1.4199 1.4216 1.4219
## 22 1 3 1 1 1 1 16 1 1
## 1.4225 1.4231 1.4233 1.4239 1.425 1.4255 1.4259 1.4266 1.4271 1.4275
## 2 2 7 1 4 1 2 12 1 1
## 1.4277 1.4278 1.4279 1.4283 1.4288 1.4299 1.4306 1.4312 1.4315 1.4316
## 1 1 3 1 1 15 1 1 1 1
## 1.4319 1.4322 1.4323 1.4325 1.4333 1.4337 1.4344 1.435 1.4357 1.4359
## 1 1 1 1 12 1 2 5 2 2
## 1.4362 1.4363 1.4366 1.4375 1.4383 1.4399 1.4412 1.4413 1.4416 1.4419
## 1 1 12 1 3 22 1 1 2 1
## 1.4428 1.4433 1.444 1.445 1.4466 1.4475 1.4481 1.4482 1.4483 1.4487
## 1 20 1 1 15 1 1 1 1 1
## 1.4498 1.45 1.4509 1.452 1.4528 1.4533 1.454 1.4544 1.4547 1.455
## 1 11 2 4 1 10 2 1 1 3
## 1.4555 1.456 1.4566 1.4575 1.4583 1.4593 1.46 1.462 1.4633 1.464
## 2 1 10 1 2 1 16 1 13 1
## 1.4651 1.4652 1.4666 1.4678 1.468 1.4688 1.4698 1.47 1.4716 1.472
## 1 1 10 1 1 1 1 14 1 2
## 1.4726 1.4733 1.475 1.476 1.4766 1.4769 1.4774 1.4775 1.4787 1.48
## 1 19 4 1 10 1 1 1 1 11
## 1.482 1.4824 1.4828 1.4833 1.4842 1.485 1.4866 1.4875 1.4877 1.4883
## 1 2 1 5 2 2 3 1 1 1
## 1.4886 1.49 1.492 1.4924 1.4927 1.4933 1.4942 1.495 1.496 1.4966
## 1 9 2 1 1 7 1 3 3 7
## 1.4972 1.5 1.5011 1.502 1.5033 1.504 1.5055 1.5066 1.5068 1.5074
## 1 10 1 1 6 2 1 12 1 1
## 1.5083 1.5088 1.51 1.5106 1.512 1.5127 1.5133 1.5142 1.5149 1.516
## 1 1 6 1 3 1 7 1 1 1
## 1.5166 1.5169 1.5176 1.5183 1.52 1.5203 1.5233 1.5249 1.5257 1.5266
## 6 1 2 1 6 1 2 1 1 2
## 1.5271 1.528 1.5283 1.53 1.5306 1.5311 1.5322 1.5333 1.5338 1.534
## 1 4 1 6 1 1 1 8 1 2
## 1.5345 1.5355 1.5357 1.536 1.5362 1.5366 1.538 1.5381 1.54 1.5403
## 1 1 1 1 1 3 2 1 13 2
## 1.5409 1.542 1.5433 1.5449 1.5453 1.5466 1.548 1.55 1.5521 1.5533
## 1 1 3 1 1 4 2 5 1 2
## 1.5549 1.5566 1.5568 1.5584 1.5586 1.56 1.5625 1.5633 1.5636 1.5646
## 3 3 1 1 1 6 1 1 1 1
## 1.5649 1.5666 1.5669 1.57 1.5733 1.5736 1.575 1.5766 1.5774 1.58
## 2 1 1 5 1 1 2 3 1 5
## 1.5803 1.5828 1.5833 1.584 1.585 1.5866 1.5882 1.59 1.592 1.5933
## 1 1 1 1 2 3 1 5 1 2
## 1.595 1.596 1.5966 1.5995 1.6 1.6037 1.604 1.605 1.606 1.6066
## 1 2 2 1 4 1 1 2 1 4
## 1.608 1.61 1.6133 1.6142 1.616 1.6166 1.618 1.6181 1.62 1.6218
## 1 2 1 1 1 2 1 1 3 1
## 1.623 1.625 1.6262 1.6279 1.6285 1.6299 1.6333 1.635 1.6365 1.6366
## 1 2 1 1 2 3 3 1 1 2
## 1.6399 1.6414 1.6416 1.6439 1.645 1.6462 1.6466 1.6472 1.6487 1.6499
## 5 1 1 1 1 1 3 1 1 1
## 1.6533 1.6537 1.6559 1.6566 1.6599 1.6633 1.6639 1.665 1.6666 1.6699
## 1 1 1 2 1 2 1 2 5 1
## 1.6733 1.6737 1.6759 1.6766 1.6779 1.6799 1.6839 1.685 1.6854 1.6866
## 5 1 1 2 1 2 1 1 1 3
## 1.6888 1.6899 1.6933 1.6966 1.6975 1.7 1.7033 1.7037 1.7066 1.7091
## 1 3 1 1 1 1 1 1 3 1
## 1.71 1.7157 1.716 1.7163 1.7164 1.7166 1.72 1.7208 1.7233 1.724
## 1 1 4 1 1 1 3 1 1 2
## 1.7266 1.73 1.7312 1.734 1.7366 1.7383 1.74 1.7433 1.744 1.745
## 1 1 1 1 1 1 1 1 1 1
## 1.7485 1.75 1.7503 1.7523 1.7549 1.756 1.7633 1.7666 1.77 1.776
## 1 2 1 1 2 1 1 2 3 1
## 1.7769 1.778 1.78 1.7833 1.7849 1.788 1.79 1.7933 1.7966 1.798
## 1 1 2 4 1 4 2 1 1 1
## 1.8 1.804 1.8066 1.808 1.81 1.812 1.8153 1.8193 1.824 1.825
## 2 1 1 1 3 2 1 1 1 1
## 1.835 1.84 1.8428 1.8466 1.85 1.8533 1.8566 1.86 1.864 1.8666
## 2 1 1 1 1 2 1 1 1 2
## 1.872 1.8799 1.8841 1.8852 1.8864 1.887 1.8879 1.8919 1.8939 1.8963
## 1 2 1 2 1 1 1 1 1 1
## 1.8966 1.9066 1.9075 1.9079 1.9133 1.9187 1.9233 1.925 1.9266 1.9359
## 2 2 1 1 1 1 1 1 2 1
## 1.9399 1.9589 1.96 1.9619 1.9633 1.9659 1.9718 1.9786 1.9833 1.9916
## 1 1 2 1 1 1 1 1 1 1
## 1.9966 2.004 2.0074 2.0166 2.0379 2.04 2.0687 2.0699 2.0733 2.075
## 2 1 1 1 1 1 1 1 1 1
## 2.084 2.085 2.088 2.0899 2.104 2.1099 2.1245 2.1292 2.1304 2.1459
## 1 1 1 1 1 1 1 1 1 1
## 2.1466 2.1499 2.16 2.165 2.1699 2.172 2.1766 2.18 2.1841 2.196
## 1 2 2 1 1 1 1 1 1 1
## 2.2015 2.204 2.206 2.212 2.217 2.22 2.2333 2.2566 2.2599 2.262
## 1 1 1 1 1 1 1 1 2 1
## 2.2657 2.27 2.2726 2.2733 2.2799 2.2933 2.313 2.3199 2.3266 2.3355
## 1 2 1 1 1 1 1 1 1 1
## 2.3477 2.35 2.3599 2.3733 2.3789 2.3858 2.39 2.3933 2.4016 2.4172
## 1 1 1 1 1 1 1 1 1 1
## 2.422 2.4233 2.4433 2.498 2.5 2.503 2.5059 2.5266 2.5363 2.5366
## 1 3 1 1 1 1 1 1 1 1
## 2.5566 2.5733 2.6133 2.616 2.6468 2.6666 2.68 2.6833 2.778 2.7799
## 1 2 1 1 1 1 1 1 1 1
## 2.7833 2.8177 2.8566 2.8574 3.0262 3.1012 3.1025 3.1699 3.184 3.3466
## 1 1 1 1 1 1 1 1 1 1
## 3.4866 3.6179 3.691 3.7033 3.8199 3.9085 3.9266 4.6407 4.7647 5.0979
## 1 1 1 1 1 2 1 1 1 1
## 5.1046 5.23 5.4483 5.9266 6.5999 7.2999 7.5819 8.5533
## 1 1 1 1 1 1 1 1here we observe the data in the floating point.
finding the less than value 3 & 7
NROW(data[which(data$x242<=3),])## [1] 6636Due to missing values here we observed 6% less than 3
NROW(data[which(data$x242<=7),])## [1] 6658here we observed 6% are less than 7
Now visualize the statistics
##Histogram
hist(data$x242,
main = "Histogram of x242",
xlab = "x242")
abline(v =mean(data$x242, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 2 has max values.
Boxplot
boxplot(data$x242,
main = toupper("Boxplot of x242"),
ylab = "x242",
col = "red")
## we found the outliers. ##Density Plot
d <- density(data$x242, na.rm = TRUE)
plot(d, main = "Kernel density of x242")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x242, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here 0 to 2 has more density there is 93% of missing values most of the values lies in b0 to 2 and the skewness is a postive because the tail will have the large values.so the varialbe has continous values.
summary of the variable x243
summary(data$x243)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 122 0 76901observe that values from 0 to 76901, median is 0 & mean is 122
Description of the variable x243
describe(data$x243)## data$x243
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 2832 0.179 122 238.2 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 400
##
## lowest : 0 1 5 6 8, highest: 41358 44360 48868 62038 76901Here the kurtosis is positive and it has large data in tails.
Total number of values excluding NA
NROW(data$x243) - NROW(data[which(is.na(data$x243)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x243)##
## 0 1 5 6 8 9 11 13 14 17 18 19
## 93636 2 3 2 2 1 3 2 1 1 2 1
## 20 22 23 24 27 31 32 34 35 36 38 39
## 1 1 2 1 1 1 1 1 1 2 1 1
## 40 44 45 49 50 52 53 58 59 63 64 65
## 1 1 1 1 1 1 1 1 3 1 4 3
## 66 67 68 70 72 74 79 80 82 83 84 87
## 2 1 1 1 1 1 3 1 1 4 2 3
## 89 90 91 92 93 94 95 96 97 98 99 100
## 4 2 1 3 2 1 4 3 5 5 5 5
## 101 102 103 104 105 106 107 109 110 111 112 113
## 4 2 4 2 6 3 3 3 4 3 1 1
## 114 116 117 118 119 120 121 122 123 124 126 127
## 1 4 1 5 2 3 4 1 3 2 1 3
## 128 129 130 132 133 134 135 136 137 138 139 140
## 2 1 3 4 2 5 2 2 3 3 1 4
## 141 142 143 144 145 146 147 148 149 150 151 152
## 2 1 2 2 1 2 4 2 2 1 1 4
## 153 154 155 156 157 159 162 163 164 165 166 167
## 2 1 1 3 1 2 2 2 2 2 1 4
## 169 171 172 173 174 176 177 178 179 181 182 183
## 1 1 3 1 2 2 1 1 1 5 2 4
## 184 185 186 187 188 189 190 191 192 193 194 195
## 4 1 1 2 2 2 1 3 2 1 1 5
## 196 197 198 199 200 201 202 203 204 205 206 208
## 1 1 1 2 1 2 2 3 3 3 2 2
## 209 210 211 212 213 214 215 216 217 218 219 220
## 2 4 2 1 2 6 6 2 2 3 2 2
## 221 222 223 224 225 226 227 228 230 231 232 234
## 2 2 2 1 2 3 1 2 2 2 6 1
## 236 237 238 239 240 241 242 243 244 245 246 248
## 5 3 3 4 2 2 2 1 2 2 1 3
## 249 250 251 252 253 254 255 256 257 258 259 260
## 5 1 3 2 3 4 4 3 5 4 3 3
## 261 262 264 265 266 267 268 269 270 272 273 274
## 2 2 1 3 3 5 8 6 6 2 3 2
## 275 276 277 278 279 280 281 282 283 284 285 286
## 5 1 3 2 5 1 6 4 6 3 6 1
## 287 288 289 290 291 292 293 294 295 296 297 298
## 5 3 4 3 3 3 1 5 5 4 2 3
## 299 300 301 302 303 304 305 306 307 308 309 311
## 4 2 4 2 4 7 1 3 2 5 3 4
## 312 313 314 315 316 317 318 319 320 321 323 324
## 3 4 5 4 5 8 2 8 3 4 1 5
## 325 326 327 328 329 330 331 332 333 334 335 336
## 6 1 1 2 4 4 1 3 7 5 4 4
## 337 338 339 340 341 342 343 344 345 346 347 348
## 7 10 13 4 5 5 8 6 7 2 5 2
## 349 350 351 352 353 354 355 356 357 358 359 360
## 3 4 7 7 7 2 8 4 6 8 15 10
## 361 362 363 364 365 366 367 368 369 370 371 372
## 10 3 11 8 4 10 9 13 10 12 15 26
## 373 374 375 376 377 378 379 380 381 382 383 384
## 7 11 11 14 25 17 10 15 11 12 9 12
## 385 386 387 388 389 390 391 392 393 394 395 396
## 8 9 15 14 11 12 15 15 19 25 13 9
## 397 398 399 400 401 402 403 404 405 406 407 408
## 11 10 8 7 4 11 5 5 4 9 12 6
## 409 410 411 412 413 414 415 416 417 418 419 420
## 10 11 14 9 12 17 18 9 9 17 21 17
## 421 422 423 424 425 426 427 428 429 430 431 432
## 13 21 15 14 24 15 8 11 17 16 17 15
## 433 434 435 436 437 438 439 440 441 442 443 444
## 22 14 11 6 12 14 16 12 10 20 12 7
## 445 446 447 448 449 450 451 452 453 454 455 456
## 3 4 7 5 11 9 8 12 7 8 6 6
## 457 458 459 460 461 462 463 464 465 466 467 468
## 3 4 5 9 5 11 5 5 8 3 5 4
## 469 470 471 472 473 474 475 476 477 478 479 480
## 5 2 5 4 10 5 3 9 7 5 3 1
## 481 482 483 484 485 486 487 488 489 490 491 492
## 3 8 5 4 2 5 10 4 8 7 7 7
## 493 494 495 496 497 498 499 500 501 502 503 504
## 4 8 2 2 4 7 3 9 8 6 6 5
## 505 506 507 508 509 510 511 512 513 514 515 516
## 2 8 2 5 5 2 2 1 1 1 4 3
## 517 518 519 520 521 522 523 524 525 526 527 528
## 6 4 3 2 4 2 6 3 3 3 2 6
## 529 530 531 532 533 534 535 536 537 538 539 540
## 6 5 3 3 3 5 9 3 6 9 4 7
## 541 542 543 544 545 547 548 549 550 551 552 553
## 2 6 9 3 6 2 5 5 3 3 1 6
## 554 555 556 557 558 560 561 562 563 564 565 566
## 3 6 5 5 3 4 3 2 1 1 4 4
## 567 568 569 570 571 572 573 574 575 576 577 578
## 7 3 6 1 4 10 5 3 2 10 2 2
## 579 580 581 582 583 584 585 586 587 588 589 590
## 5 2 3 4 4 3 2 3 5 4 6 6
## 591 592 593 594 595 596 598 599 600 601 602 603
## 4 5 5 5 4 6 5 8 1 6 2 3
## 604 605 606 607 608 609 610 611 612 613 614 615
## 6 7 1 2 3 4 3 2 2 3 3 2
## 616 618 620 621 622 623 624 625 626 627 628 630
## 3 8 5 1 5 1 3 4 6 4 1 6
## 631 632 633 634 635 636 637 638 639 640 641 642
## 4 2 2 5 2 4 3 1 1 2 1 8
## 643 644 645 646 647 648 649 650 651 652 653 654
## 3 3 3 1 6 5 3 3 4 3 6 4
## 655 656 657 658 659 660 661 662 663 664 665 666
## 5 3 1 3 3 4 5 5 5 3 5 4
## 667 668 669 670 671 672 674 675 676 677 678 679
## 4 2 3 5 4 7 7 2 2 4 4 1
## 680 681 682 683 684 685 686 687 688 689 690 691
## 3 4 1 2 1 5 5 3 2 3 5 4
## 692 693 694 695 696 697 698 699 700 701 702 703
## 2 4 2 4 4 4 5 2 1 3 1 6
## 704 705 706 707 708 709 710 711 712 713 714 715
## 2 3 1 4 2 4 2 1 1 3 3 2
## 716 718 720 721 722 723 724 725 727 728 729 730
## 2 2 3 1 3 1 1 1 5 1 5 2
## 731 733 734 735 736 737 739 740 741 742 743 745
## 3 4 1 1 2 1 2 3 3 1 1 2
## 746 747 748 749 750 751 752 753 754 755 756 757
## 1 2 1 2 3 4 4 1 3 3 3 3
## 758 759 760 761 762 763 764 765 766 767 768 769
## 5 1 2 6 5 4 3 9 6 5 3 5
## 770 771 772 773 774 775 776 777 778 779 780 781
## 3 4 2 1 2 1 5 1 2 4 2 2
## 782 783 784 785 786 787 788 789 790 791 792 793
## 1 4 2 7 2 2 3 3 3 3 4 3
## 795 796 797 798 799 800 801 802 803 804 805 806
## 4 2 3 2 2 6 2 1 7 3 5 4
## 807 808 809 810 811 812 813 814 815 816 817 818
## 1 1 3 2 2 1 5 3 2 3 1 3
## 822 823 824 825 826 827 828 829 830 831 832 833
## 2 6 4 2 1 1 2 2 1 3 8 2
## 834 835 837 838 839 840 841 842 843 844 846 847
## 6 2 4 5 3 3 1 4 1 4 4 2
## 848 849 850 851 852 853 854 855 856 857 858 859
## 4 6 2 3 3 3 2 4 1 5 1 4
## 860 861 862 863 864 865 866 867 868 869 870 871
## 2 4 5 5 2 3 2 3 3 2 5 3
## 872 873 874 875 877 878 879 880 881 882 883 884
## 4 2 4 3 1 2 2 5 3 1 2 4
## 885 886 887 888 889 890 891 892 893 894 895 896
## 3 2 2 2 2 4 2 3 4 3 2 3
## 897 898 899 900 901 902 903 904 905 906 908 910
## 4 2 1 1 2 4 2 3 2 1 3 2
## 912 913 916 917 918 919 920 921 923 924 926 927
## 1 2 2 3 2 1 5 1 1 3 2 3
## 928 929 930 931 932 933 934 935 936 937 938 940
## 2 3 1 2 3 1 4 2 6 1 2 6
## 941 942 943 944 945 946 947 949 950 951 952 954
## 3 1 3 4 2 2 3 1 3 5 2 4
## 955 956 957 958 959 960 961 962 963 964 965 966
## 1 3 1 3 1 3 1 3 2 2 1 1
## 967 968 969 970 971 972 973 974 975 976 977 978
## 2 1 1 2 3 2 1 2 5 3 2 1
## 979 980 981 982 983 985 986 987 988 990 991 993
## 1 1 2 2 3 1 2 4 2 1 1 3
## 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005
## 3 1 5 1 5 4 4 3 2 3 2 3
## 1006 1007 1008 1009 1011 1012 1013 1015 1016 1017 1018 1019
## 3 1 1 2 4 2 3 5 1 4 1 4
## 1020 1021 1022 1023 1024 1025 1026 1027 1029 1030 1031 1032
## 2 1 1 3 4 2 1 1 1 1 1 3
## 1033 1034 1035 1036 1037 1039 1040 1041 1042 1043 1044 1045
## 3 2 2 1 3 2 1 1 2 3 1 1
## 1046 1047 1048 1049 1050 1051 1052 1054 1055 1056 1057 1058
## 3 4 2 2 1 1 2 1 5 2 1 3
## 1059 1060 1061 1062 1063 1064 1066 1067 1068 1069 1070 1071
## 1 2 3 2 2 2 2 1 2 4 1 2
## 1072 1073 1074 1075 1078 1079 1080 1081 1082 1084 1085 1086
## 3 4 2 1 1 3 4 4 3 2 4 2
## 1087 1088 1089 1090 1091 1092 1094 1095 1096 1098 1100 1101
## 1 3 3 4 2 3 3 1 3 1 1 7
## 1102 1103 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114
## 5 2 3 1 2 1 2 4 5 2 4 1
## 1115 1116 1117 1118 1119 1120 1121 1123 1124 1127 1128 1129
## 6 1 2 1 1 2 2 3 3 1 1 3
## 1130 1131 1132 1133 1134 1135 1136 1138 1139 1140 1141 1142
## 1 1 1 6 4 1 1 2 3 5 1 1
## 1143 1145 1147 1148 1149 1150 1152 1153 1154 1155 1156 1157
## 2 2 1 2 4 1 1 3 2 2 3 2
## 1158 1159 1160 1161 1162 1165 1166 1167 1168 1169 1170 1171
## 1 4 2 1 2 1 3 2 2 2 5 2
## 1174 1177 1178 1179 1180 1181 1182 1183 1186 1187 1190 1192
## 3 1 1 1 3 3 2 2 2 5 2 1
## 1193 1195 1196 1199 1201 1202 1204 1206 1208 1209 1210 1211
## 2 2 3 1 1 1 2 1 2 1 1 1
## 1212 1213 1216 1217 1219 1220 1222 1224 1225 1226 1227 1228
## 1 2 3 1 2 3 2 2 1 1 2 1
## 1229 1230 1231 1232 1233 1234 1236 1237 1238 1240 1241 1242
## 1 3 1 1 4 1 2 1 1 3 1 1
## 1244 1245 1246 1247 1248 1249 1251 1252 1253 1254 1255 1256
## 1 1 2 4 2 2 1 2 1 1 2 2
## 1258 1259 1260 1262 1263 1266 1267 1271 1272 1274 1275 1276
## 1 1 2 2 4 1 2 2 1 2 1 2
## 1278 1279 1280 1283 1284 1285 1286 1287 1288 1289 1290 1291
## 1 1 2 5 2 3 2 3 4 2 1 3
## 1292 1293 1294 1295 1296 1298 1299 1300 1301 1302 1303 1306
## 1 2 2 4 1 4 2 1 1 2 1 2
## 1307 1308 1312 1313 1314 1316 1317 1318 1319 1320 1322 1323
## 3 1 1 1 1 1 2 1 3 2 1 1
## 1326 1327 1328 1329 1330 1331 1332 1333 1338 1339 1340 1341
## 3 2 1 2 1 2 2 1 4 3 2 2
## 1345 1346 1349 1351 1352 1354 1355 1356 1357 1358 1359 1360
## 2 2 1 1 3 2 3 2 1 2 2 2
## 1361 1364 1365 1366 1368 1369 1370 1371 1375 1376 1379 1380
## 1 2 1 1 1 3 2 1 2 1 1 1
## 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1395 1396
## 2 1 1 1 2 3 2 2 2 2 2 1
## 1398 1399 1400 1402 1403 1405 1406 1408 1409 1410 1412 1413
## 1 1 2 1 1 1 1 2 1 1 4 2
## 1414 1416 1417 1418 1419 1423 1424 1425 1426 1427 1428 1431
## 2 1 1 1 3 2 1 1 1 2 2 2
## 1436 1438 1442 1443 1448 1449 1450 1451 1452 1453 1455 1456
## 2 2 2 2 1 1 1 2 2 1 1 3
## 1458 1460 1461 1465 1467 1469 1471 1473 1474 1475 1477 1478
## 3 1 1 2 2 2 1 1 2 2 1 1
## 1480 1482 1483 1484 1485 1486 1489 1492 1494 1495 1497 1499
## 2 1 1 2 1 3 1 1 1 3 2 1
## 1502 1503 1504 1505 1507 1508 1509 1512 1514 1517 1518 1520
## 1 1 2 1 2 1 1 1 1 1 1 2
## 1522 1523 1526 1529 1530 1533 1535 1536 1538 1540 1541 1542
## 1 1 3 1 2 1 3 1 2 1 5 3
## 1543 1544 1545 1546 1548 1551 1552 1554 1555 1556 1557 1560
## 1 1 1 2 2 2 3 1 3 1 1 1
## 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1572 1573
## 1 2 1 1 2 2 2 1 2 2 3 2
## 1574 1576 1577 1579 1583 1586 1588 1590 1592 1594 1595 1596
## 2 1 2 2 4 1 3 2 2 4 1 2
## 1597 1598 1599 1600 1601 1604 1605 1606 1607 1608 1609 1612
## 2 1 1 2 3 1 1 1 1 1 1 2
## 1613 1615 1616 1617 1620 1621 1622 1623 1624 1625 1626 1627
## 4 2 1 1 1 2 2 1 1 1 2 1
## 1628 1631 1633 1635 1640 1644 1645 1647 1650 1651 1652 1655
## 1 1 1 2 2 1 1 1 3 2 2 2
## 1658 1660 1661 1664 1667 1668 1669 1670 1671 1673 1674 1676
## 4 3 1 1 1 1 2 1 2 1 1 1
## 1680 1681 1683 1685 1688 1689 1690 1693 1694 1695 1696 1697
## 2 1 2 3 1 1 2 1 1 3 1 2
## 1700 1702 1704 1706 1707 1708 1709 1710 1714 1716 1717 1720
## 2 2 3 2 1 2 1 1 1 2 3 1
## 1722 1723 1724 1725 1726 1728 1729 1730 1731 1733 1736 1738
## 1 1 2 1 2 2 1 3 1 1 1 1
## 1741 1743 1746 1748 1749 1750 1751 1752 1754 1755 1763 1764
## 2 1 2 1 1 1 2 1 1 2 1 1
## 1765 1767 1768 1770 1771 1772 1773 1774 1775 1776 1777 1778
## 2 1 1 4 1 1 1 1 1 3 1 1
## 1779 1780 1782 1784 1785 1786 1787 1788 1789 1790 1792 1793
## 1 1 1 1 2 2 1 1 1 1 1 2
## 1796 1804 1806 1808 1809 1810 1812 1814 1816 1818 1819 1820
## 2 1 2 2 2 2 2 1 4 1 1 2
## 1822 1823 1827 1829 1830 1834 1835 1838 1840 1841 1842 1847
## 2 1 1 1 1 1 1 2 2 2 1 1
## 1848 1849 1850 1851 1852 1854 1855 1856 1859 1861 1862 1864
## 1 1 1 1 3 2 1 1 1 1 1 1
## 1866 1868 1869 1870 1871 1872 1876 1877 1882 1884 1885 1886
## 3 1 1 1 1 2 1 2 1 2 1 2
## 1887 1890 1892 1893 1896 1897 1898 1900 1901 1903 1904 1906
## 2 1 1 2 3 1 2 1 2 1 1 2
## 1910 1911 1912 1917 1920 1922 1923 1924 1927 1928 1929 1930
## 2 1 1 1 2 1 1 2 1 2 1 1
## 1939 1940 1942 1944 1946 1948 1949 1950 1952 1955 1960 1961
## 2 2 1 1 1 1 1 1 4 1 1 1
## 1964 1966 1972 1975 1976 1977 1980 1982 1983 1985 1991 1992
## 1 1 3 1 1 1 1 3 1 2 1 3
## 1993 1996 1997 2000 2001 2005 2006 2009 2010 2011 2013 2025
## 1 1 1 1 2 1 1 4 2 1 1 1
## 2026 2027 2029 2030 2031 2034 2036 2046 2047 2048 2049 2050
## 4 3 1 1 1 1 2 1 1 1 1 1
## 2051 2052 2056 2060 2062 2065 2072 2073 2075 2076 2077 2078
## 3 2 1 1 1 1 1 1 1 1 1 3
## 2079 2082 2084 2085 2086 2091 2092 2093 2095 2097 2098 2100
## 1 1 1 1 2 2 1 1 1 1 1 1
## 2101 2103 2105 2106 2108 2109 2110 2111 2113 2116 2118 2119
## 2 2 1 1 2 1 1 1 2 1 1 1
## 2122 2124 2126 2128 2129 2134 2135 2136 2140 2145 2146 2148
## 4 1 2 1 1 1 1 1 2 1 1 1
## 2149 2151 2153 2156 2159 2164 2165 2167 2168 2170 2180 2181
## 1 1 1 1 1 1 1 1 1 1 2 3
## 2182 2184 2185 2189 2193 2194 2201 2202 2203 2204 2205 2207
## 1 1 1 2 2 2 1 1 1 2 1 2
## 2208 2209 2211 2214 2216 2217 2218 2220 2221 2225 2230 2231
## 1 1 1 1 1 1 3 1 1 3 1 2
## 2233 2237 2238 2239 2241 2242 2247 2250 2251 2252 2254 2257
## 1 1 1 1 2 1 1 1 1 2 1 4
## 2261 2264 2267 2275 2276 2277 2278 2281 2285 2291 2293 2294
## 1 1 1 1 1 1 1 1 1 1 5 1
## 2297 2298 2299 2302 2304 2313 2317 2318 2321 2324 2325 2328
## 1 1 2 1 1 1 2 1 2 1 1 2
## 2329 2330 2331 2335 2337 2344 2345 2352 2353 2355 2357 2358
## 1 1 3 1 1 1 1 2 1 1 2 2
## 2361 2366 2370 2371 2372 2374 2376 2381 2384 2388 2389 2390
## 1 1 1 1 1 1 2 1 1 1 1 3
## 2394 2396 2397 2403 2404 2412 2418 2420 2421 2423 2424 2428
## 1 1 1 1 2 1 2 2 1 2 1 1
## 2429 2433 2436 2443 2444 2447 2448 2449 2452 2454 2460 2471
## 1 1 1 1 1 1 1 1 1 1 1 1
## 2472 2476 2485 2487 2488 2490 2491 2499 2500 2503 2507 2513
## 1 1 1 1 1 1 2 2 1 2 1 1
## 2515 2517 2518 2521 2525 2526 2527 2529 2530 2532 2533 2536
## 1 1 1 2 1 2 1 1 2 1 3 2
## 2539 2542 2543 2547 2549 2550 2551 2552 2553 2555 2557 2558
## 1 3 1 1 1 1 1 1 1 2 1 1
## 2559 2560 2561 2562 2564 2565 2566 2568 2575 2576 2577 2584
## 1 2 1 2 1 1 2 1 1 1 1 2
## 2585 2586 2587 2592 2593 2594 2595 2604 2605 2609 2611 2615
## 1 1 2 1 1 1 2 2 2 2 1 2
## 2616 2618 2619 2620 2622 2629 2630 2632 2633 2634 2636 2637
## 2 2 1 1 1 1 3 1 1 1 1 1
## 2639 2640 2644 2645 2646 2647 2649 2650 2652 2654 2657 2660
## 1 1 2 1 2 1 1 1 1 1 2 1
## 2666 2669 2670 2673 2675 2676 2681 2683 2686 2687 2696 2701
## 1 1 2 2 2 1 1 1 1 1 1 2
## 2703 2704 2706 2707 2710 2711 2712 2713 2717 2719 2726 2733
## 3 2 1 2 1 1 1 1 2 2 1 1
## 2736 2737 2739 2743 2744 2746 2750 2758 2762 2767 2768 2772
## 2 1 1 1 2 1 1 1 1 1 1 1
## 2776 2780 2784 2786 2787 2789 2790 2791 2792 2793 2795 2796
## 1 3 1 1 1 1 2 2 2 1 1 1
## 2804 2805 2808 2811 2813 2814 2815 2817 2820 2827 2830 2831
## 2 2 1 1 1 1 1 1 2 1 1 2
## 2833 2835 2838 2839 2844 2850 2851 2855 2859 2869 2873 2878
## 1 1 1 3 1 3 2 1 1 3 2 1
## 2881 2882 2884 2889 2891 2895 2897 2898 2907 2922 2926 2931
## 1 2 1 1 1 2 3 2 1 2 1 1
## 2933 2937 2940 2943 2953 2955 2956 2957 2960 2964 2965 2968
## 1 1 2 1 1 2 1 1 1 1 2 1
## 2970 2973 2977 2987 2988 2992 2996 3001 3002 3011 3012 3031
## 1 1 1 1 1 2 1 1 1 2 1 1
## 3032 3045 3050 3053 3055 3060 3063 3066 3067 3072 3073 3076
## 1 1 1 2 1 1 2 3 1 1 2 1
## 3078 3082 3084 3087 3093 3094 3105 3106 3111 3112 3114 3115
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3119 3120 3124 3126 3130 3131 3134 3136 3137 3143 3147 3152
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3153 3160 3165 3166 3173 3177 3180 3186 3189 3191 3193 3204
## 1 1 1 2 1 1 1 3 1 1 2 1
## 3205 3208 3211 3215 3218 3219 3220 3224 3235 3238 3239 3240
## 1 1 2 1 1 1 1 1 1 2 1 1
## 3247 3248 3250 3253 3258 3261 3263 3264 3269 3271 3273 3277
## 1 2 1 1 1 1 1 1 1 1 1 2
## 3283 3285 3286 3297 3302 3303 3313 3314 3315 3318 3319 3322
## 1 1 1 1 1 2 1 1 1 1 1 1
## 3326 3327 3330 3333 3335 3349 3356 3361 3364 3367 3369 3370
## 1 1 1 2 1 1 2 1 1 1 2 1
## 3371 3375 3376 3378 3381 3387 3396 3399 3404 3407 3408 3409
## 1 1 1 1 1 1 1 1 1 1 2 1
## 3412 3415 3426 3429 3432 3433 3434 3435 3436 3449 3451 3454
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3465 3481 3484 3489 3491 3493 3495 3500 3503 3505 3510 3511
## 1 3 1 2 1 1 2 2 1 1 1 1
## 3520 3523 3524 3529 3534 3543 3551 3554 3556 3573 3576 3577
## 1 1 1 1 1 1 1 2 1 1 1 1
## 3578 3590 3596 3604 3606 3609 3614 3616 3617 3619 3622 3626
## 1 1 1 1 1 1 1 1 1 1 2 1
## 3628 3631 3636 3640 3642 3643 3646 3664 3669 3671 3673 3681
## 1 1 1 1 1 1 1 1 1 1 1 2
## 3702 3705 3710 3711 3714 3725 3732 3734 3737 3738 3741 3743
## 1 1 1 1 2 1 1 1 1 1 2 3
## 3745 3749 3750 3775 3786 3788 3790 3791 3806 3807 3817 3825
## 1 2 1 1 1 1 1 2 1 1 1 1
## 3827 3831 3833 3836 3846 3855 3857 3865 3870 3878 3879 3884
## 1 1 2 1 1 1 1 1 2 1 1 1
## 3900 3903 3921 3922 3943 3945 3952 3956 3962 3964 3966 3971
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3993 4010 4013 4016 4023 4024 4035 4037 4038 4049 4056 4057
## 1 1 1 1 2 2 1 1 1 1 2 1
## 4059 4060 4070 4074 4090 4091 4094 4097 4104 4105 4109 4112
## 1 1 2 1 1 1 1 1 1 1 1 1
## 4115 4116 4118 4119 4131 4141 4144 4161 4162 4165 4175 4189
## 2 1 1 1 1 1 1 1 1 1 1 1
## 4191 4192 4201 4209 4226 4231 4235 4242 4248 4255 4256 4261
## 1 2 1 2 1 1 2 1 1 1 1 1
## 4263 4267 4269 4278 4287 4305 4315 4319 4331 4342 4344 4345
## 1 1 1 1 1 1 2 1 1 2 1 1
## 4351 4379 4404 4406 4413 4415 4416 4418 4419 4421 4426 4428
## 1 1 1 2 1 1 2 1 1 2 1 1
## 4434 4444 4445 4448 4451 4454 4459 4466 4497 4504 4505 4518
## 1 2 1 1 1 1 1 1 1 1 2 1
## 4532 4535 4538 4546 4547 4566 4569 4573 4577 4578 4581 4586
## 1 1 1 1 1 1 1 1 1 1 1 2
## 4597 4601 4606 4615 4633 4637 4648 4651 4666 4685 4715 4716
## 1 1 1 2 1 1 1 1 1 1 1 1
## 4738 4743 4745 4749 4754 4757 4759 4774 4784 4789 4796 4800
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4810 4812 4820 4822 4844 4852 4860 4862 4876 4887 4890 4901
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4903 4905 4907 4915 4918 4921 4934 4943 4953 4962 4966 4967
## 1 1 1 1 1 1 1 1 1 1 1 1
## 4974 4995 4997 5001 5007 5012 5015 5028 5029 5033 5037 5047
## 1 1 1 1 1 1 1 1 1 1 1 1
## 5053 5058 5062 5064 5065 5068 5094 5096 5098 5103 5107 5114
## 1 1 1 1 1 1 1 1 1 1 1 1
## 5116 5117 5118 5128 5141 5148 5157 5164 5172 5181 5184 5197
## 1 2 1 1 1 1 1 1 1 1 1 1
## 5198 5201 5205 5207 5210 5211 5217 5221 5225 5236 5250 5252
## 1 1 1 1 1 2 1 1 1 1 1 1
## 5260 5264 5266 5268 5285 5286 5291 5296 5297 5304 5312 5313
## 1 1 1 1 1 1 1 2 1 1 1 1
## 5329 5343 5353 5365 5369 5376 5380 5384 5412 5415 5434 5464
## 1 1 1 1 1 1 1 1 1 1 1 2
## 5484 5486 5489 5494 5505 5542 5562 5569 5582 5595 5599 5603
## 1 1 1 1 1 1 1 2 1 1 1 1
## 5608 5622 5643 5645 5646 5664 5671 5691 5692 5695 5734 5739
## 1 1 1 1 1 1 2 1 1 1 1 1
## 5744 5756 5779 5801 5813 5817 5818 5842 5862 5877 5880 5905
## 1 1 1 1 1 2 1 1 1 1 1 1
## 5920 5922 5927 5939 5947 5981 5994 6000 6005 6007 6028 6030
## 1 1 1 1 2 1 1 1 1 1 2 1
## 6034 6042 6049 6051 6055 6064 6084 6094 6095 6097 6103 6104
## 1 1 2 1 1 1 1 1 1 1 1 1
## 6120 6123 6142 6143 6149 6153 6169 6191 6197 6217 6221 6222
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6226 6229 6232 6237 6247 6251 6252 6265 6277 6299 6307 6314
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6317 6347 6349 6358 6391 6405 6406 6418 6439 6440 6450 6479
## 1 1 2 1 1 1 1 1 1 1 1 1
## 6500 6507 6509 6543 6547 6584 6607 6625 6635 6641 6663 6681
## 1 1 1 1 1 1 1 1 1 2 1 1
## 6700 6704 6741 6762 6778 6810 6813 6840 6848 6857 6877 6881
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6890 6895 6899 6938 6952 7026 7029 7035 7058 7059 7064 7073
## 1 1 1 1 1 1 1 1 1 1 1 1
## 7102 7103 7106 7117 7136 7137 7166 7170 7200 7217 7228 7232
## 1 1 1 1 1 1 2 1 1 1 1 1
## 7249 7250 7259 7281 7287 7355 7364 7376 7383 7394 7421 7445
## 1 1 1 1 1 1 1 1 1 1 1 1
## 7452 7466 7481 7487 7495 7506 7531 7533 7545 7588 7610 7611
## 1 1 1 2 3 1 1 1 1 2 1 1
## 7628 7651 7652 7656 7657 7661 7665 7690 7706 7709 7734 7744
## 1 1 1 1 1 1 1 1 1 1 1 1
## 7746 7748 7755 7789 7797 7811 7822 7854 7861 7865 7876 7878
## 1 1 2 1 1 1 1 1 1 1 1 1
## 7900 7911 7920 7941 8000 8020 8029 8036 8054 8057 8155 8157
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8196 8201 8208 8229 8278 8292 8293 8311 8314 8320 8332 8335
## 1 1 1 1 1 1 2 1 1 1 1 1
## 8338 8347 8348 8368 8398 8440 8442 8444 8471 8473 8478 8487
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8498 8629 8673 8702 8764 8767 8822 8859 8864 8867 8869 8918
## 1 1 1 1 1 1 1 1 1 1 2 1
## 8929 8933 8938 8943 8950 8965 8981 9007 9009 9018 9025 9063
## 1 1 1 1 1 1 1 1 1 1 1 1
## 9102 9106 9123 9135 9152 9177 9285 9299 9358 9405 9411 9459
## 1 1 1 1 1 1 2 1 1 1 1 2
## 9464 9468 9469 9482 9484 9503 9509 9567 9580 9649 9715 9763
## 1 1 1 1 1 2 2 1 1 1 1 1
## 9766 9789 9872 9892 9900 9975 9994 10006 10016 10024 10034 10053
## 1 1 2 1 1 1 1 1 1 1 1 1
## 10057 10090 10127 10150 10157 10283 10375 10400 10403 10407 10422 10505
## 1 1 1 1 1 1 1 1 1 1 1 1
## 10582 10619 10694 10709 10758 10766 10797 10803 10864 10871 10950 10957
## 1 1 1 1 1 1 1 1 1 1 1 1
## 10970 11007 11101 11125 11160 11171 11188 11209 11228 11231 11265 11301
## 1 1 1 1 1 2 1 1 1 1 1 1
## 11385 11400 11410 11449 11532 11576 11579 11642 11690 11699 11845 11890
## 1 1 1 2 1 1 1 1 1 1 1 1
## 12130 12176 12189 12204 12223 12260 12372 12440 12482 12557 12664 12776
## 1 1 1 1 1 1 1 1 1 1 1 1
## 12859 12875 12956 13095 13176 13220 13324 13341 13371 13385 13453 13484
## 1 1 1 1 1 1 1 1 1 1 1 1
## 13544 13603 13640 13769 13794 13804 13915 13997 14000 14008 14025 14030
## 1 1 1 1 1 1 1 1 1 1 1 1
## 14114 14366 14372 14436 14495 14778 14920 14934 14978 15027 15037 15141
## 1 1 1 1 1 1 1 1 1 1 1 1
## 15289 15412 15432 15476 15504 15562 15595 15629 15658 15960 16013 16025
## 1 1 1 1 1 1 1 1 1 1 1 2
## 16052 16264 16273 16288 16498 16565 16604 16612 16623 16667 16717 16722
## 1 1 1 1 1 1 1 1 1 1 1 1
## 16778 17048 17090 17167 17475 17500 17756 17790 17862 18058 18656 18848
## 1 1 1 1 1 1 1 1 1 1 1 2
## 18859 18948 19036 19124 19289 19299 19957 20170 20409 20487 20512 20523
## 1 1 1 1 1 1 1 1 1 2 1 1
## 20773 20914 21709 21996 22290 22925 23145 23198 23838 23953 24193 24210
## 1 1 1 1 1 1 2 1 1 1 1 1
## 24408 24548 25002 25401 27037 27554 27969 28232 28287 28489 28579 29089
## 1 1 1 1 1 1 1 1 1 1 1 1
## 31234 32547 34245 34767 36760 36842 37069 41358 44360 48868 62038 76901
## 1 1 1 1 1 1 1 1 1 1 1 1here we observe that 93% of values are 0.
finding the less than value 150 & 1500
NROW(data[which(data$x243<=150),])## [1] 93867here we observed 93% less than 150
NROW(data[which(data$x243<=1500),])## [1] 98072here we observed 98% are less than 1500.
Now visualize the statistics
Histogram
hist(data$x243,
main = "Histogram of x243",
xlab = "x243")
abline(v =mean(data$x243, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10000 has more frequnecy.
Boxplot
boxplot(data$x243,
main = toupper("Boxplot of x243"),
ylab = "x243",
col = "red")
## we got the outliers.
Density Plot
d <- density(data$x243, na.rm = TRUE)
plot(d, main = "Kernel density of x243")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x243, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## here 0 has more density there is no missing values most of the values lies in b0 to 10 and the skewness is a postive because the tail will have the large values.so the variable is descrete values.
summary of the variable x244
summary(data$x244)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.6326 1.0000 1.0000observe that values from 0 to 1, median is 1 & mean is 0.6326
Description of the variable x244
describe(data$x244)## data$x244
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.697 63256 0.6326 0.4649Here the skewness is negative.
Total number of values excluding NA
NROW(data$x244) - NROW(data[which(is.na(data$x244)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x244)##
## 0 1
## 36744 63256here we observe that only 0 and 1 values clearly these are binary values.
finding the less than value 0
NROW(data[which(data$x244<=0),])## [1] 36744here we observed 36% less than 0
Now visualize the statistics
Histogram
hist(data$x244,
main = "Histogram of x244",
xlab = "x244")
abline(v =mean(data$x244, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 and 1 values existed.
Boxplot
boxplot(data$x244,
main = toupper("Boxplot of x244"),
ylab = "x244",
col = "red")
## wee did not gt any outliers. ##Density Plot
d <- density(data$x244, na.rm = TRUE)
plot(d, main = "Kernel density of x244")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x244, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here there is no missing values most of the values are 0 & 1 and the skewness is a Negative because the values increases left to right.so this variabl clearly show that binary.
summary of the variable x245
summary(data$x245)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.7294 1.0000 1.0000observe that values from 0 to 1, median is 1 & mean is 0.7294
Description of the variable x245
describe(data$x245)## data$x245
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.592 72937 0.7294 0.3948here the skewness is negative.
Total number of values excluding NA
NROW(data$x245) - NROW(data[which(is.na(data$x245)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x245)##
## 0 1
## 27063 72937here we observe that 27% of values are 0 and 72% of values are 1.
Now visualize the statistics
Histogram
hist(data$x245,
main = "Histogram of x245",
xlab = "x245")
abline(v =mean(data$x245, na.rm = TRUE),col = "red",
lwd = 2)
## observe that this variable has binary values.
Boxplot
boxplot(data$x245,
main = toupper("Boxplot of x245"),
ylab = "x245",
col = "red")
## we did not get any outliers
Density Plot
d <- density(data$x245, na.rm = TRUE)
plot(d, main = "Kernel density of x245")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x245, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Herethere is no missing values most of the values are 0 & 1 and the skewness is a negative because the values are increasing from left to right.so the variable has a binary values.
summary of the variable x246
summary(data$x246)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.7372 1.0000 1.0000observe that values from 0 to 1, median is 1 & mean is 0.7372
Description of the variable x246
describe(data$x246)## data$x246
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.581 73722 0.7372 0.3875Here the skewness is negative.
Total number of values excluding NA
NROW(data$x246) - NROW(data[which(is.na(data$x246)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x246)##
## 0 1
## 26278 73722here we observe that 26% of 0 values and 73% of 1 values.
Now visualize the statistics
Histogram
hist(data$x246,
main = "Histogram of x246",
xlab = "x246")
abline(v =mean(data$x246, na.rm = TRUE),col = "red",
lwd = 2)
## we observe that this has binary values
Boxplot
boxplot(data$x246,
main = toupper("Boxplot of x246"),
ylab = "x246",
col = "red")
## we did not get any outliers.
Density Plot
d <- density(data$x246, na.rm = TRUE)
plot(d, main = "Kernel density of x246")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x246, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## here there is no missing values most of the values are 0 & 1 and the skewness is a negative because the values increasing from left to right.so the varible has binary values.
summary of the variable x247
summary(data$x247)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 1.0000 1.0000 0.7506 1.0000 1.0000observe that values from 0 to 1, median is 0 & mean is 0.7506
Description of the variable x247
describe(data$x247)## data$x247
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.562 75061 0.7506 0.3744Here the skewness is negative.
Total number of values excluding NA
NROW(data$x247) - NROW(data[which(is.na(data$x247)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x247)##
## 0 1
## 24939 75061here we observe that 25 % of values are 0 & 75% of values are 1
Now visualize the statistics
Histogram
hist(data$x247,
main = "Histogram of x247",
xlab = "x247")
abline(v =mean(data$x247, na.rm = TRUE),col = "red",
lwd = 2)
## observe that it has only 0 and 1.
Boxplot
boxplot(data$x247,
main = toupper("Boxplot of x247"),
ylab = "x247",
col = "red")
## we got only on outlier
Density Plot
d <- density(data$x247, na.rm = TRUE)
plot(d, main = "Kernel density of x247")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x247, na.rm = TRUE),col = "royalblue",
lwd = 2)
observation:-
Here there is no missing values .so the variable has binary values.
summary of the variable x248
summary(data$x248)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2069 0.0000 1.0000observe that values from 0 to 1, median is 0 & mean is 0.2069
Description of the variable x248
describe(data$x248)## data$x248
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.492 20695 0.2069 0.3282Here the kurtosis is positive so its has large data in tails.
Total number of values excluding NA
NROW(data$x248) - NROW(data[which(is.na(data$x248)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x248)##
## 0 1
## 79305 20695here we observe that 79% of values are 0 and 21% of values are 1
Now visualize the statistics
Histogram
hist(data$x248,
main = "Histogram of x248",
xlab = "x248")
abline(v =mean(data$x248, na.rm = TRUE),col = "red",
lwd = 2)
## observe that this variablee has binary values.
Boxplot
boxplot(data$x248,
main = toupper("Boxplot of x248"),
ylab = "x248",
col = "red")
## we got the outlier.
Density Plot
d <- density(data$x248, na.rm = TRUE)
plot(d, main = "Kernel density of x248")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x248, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## here there is no missing values the skewness is a postive because the tail will have the large values.so the variabl has binary values.
summary of the variable x249
summary(data$x249)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3413 1.0000 1.0000observe that values from 0 to 1, median is 0 & mean is 0.3413
Description of the variable x249
describe(data$x249)## data$x249
## n missing distinct Info Sum Mean Gmd
## 1e+05 0 2 0.674 34135 0.3413 0.4497#Here the kurtosis is negative.
Total number of values excluding NA
NROW(data$x249) - NROW(data[which(is.na(data$x249)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x249)##
## 0 1
## 65865 34135here we observe that 65% of values are 0 and 34% of values are 1
Now visualize the statistics
Histogram
hist(data$x249,
main = "Histogram of x249",
xlab = "x249")
abline(v =mean(data$x249, na.rm = TRUE),col = "red",
lwd = 2)
## observe that it clearly saying that it has binary values.
Boxplot
boxplot(data$x249,
main = toupper("Boxplot of x249"),
ylab = "x249",
col = "red")
## we did not gt any outliers
Density Plot
d <- density(data$x249, na.rm = TRUE)
plot(d, main = "Kernel density of x249")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x249, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation:- ## Here there is no missing values and skewness is a postive because the tail will have the large values.so the variable has binary values.
summary of the variable x250
summary(data$x250)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.9851 1.0000 62.0000observe that values from 0 to 62, median is 0 & mean is 0.9851
Description of the variable x250
describe(data$x250)## data$x250
## n missing distinct Info Mean Gmd .05 .10
## 1e+05 0 34 0.712 0.9851 1.544 0 0
## .25 .50 .75 .90 .95
## 0 0 1 3 5
##
## lowest : 0 1 2 3 4, highest: 30 31 33 36 62Here the kurtosis is positive and its has large data in tails.
Total number of values excluding NA
NROW(data$x250) - NROW(data[which(is.na(data$x250)),])## [1] 100000Here the value is 100000 because there is no missing values
table(data$x250)##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 65865 10308 8179 5739 4010 2426 1464 823 474 270 152 80
## 12 13 14 15 16 17 18 19 20 21 22 23
## 62 34 23 20 11 13 12 6 2 3 7 4
## 24 26 27 28 29 30 31 33 36 62
## 1 2 1 2 1 1 2 1 1 1here we observe that 65% of values are 0.
finding the less than value 1 & 7
NROW(data[which(data$x250<=1),])## [1] 76173## here we observed 76% less than 1NROW(data[which(data$x250<=7),])## [1] 98814## here we observed 98% are less than 7Now visualize the statistics
##Histogram
hist(data$x250,
main = "Histogram of x250",
xlab = "x250")
abline(v =mean(data$x250, na.rm = TRUE),col = "red",
lwd = 2)
## observe that 0 to 10 has max values.
Boxplot
boxplot(data$x250,
main = toupper("Boxplot of x250"),
ylab = "x250",
col = "red")
## we found the outliers.
Density Plot
d <- density(data$x250, na.rm = TRUE)
plot(d, main = "Kernel density of x250")
polygon(d, col = "red", border = "blue")
abline(v =mean(data$x250, na.rm = TRUE),col = "royalblue",
lwd = 2)
##observation- ## Here 0 has more density there is no missing values most of the values lies in b0 to 7 and the skewness is a postive because the tail will have the large values. so the variable has descrete values.
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.